summary of a research paper ai

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If GPT had a PhD

• 100,000 words summarized
• First article summarized per month can be up to 200,000 words
• 50 documents indexed for semantic search
• 100 Chat Messages
• Unlimited article searches
• Import and summarize references with the click of a button
• 1,000,000 words summarized per month
• Maximum document length of 200,000 words
• Unlimited bulk summaries
• 10,000 chat messages per month
• 1,000 documents indexed for semantic search

28 Best Tools To Summarize Research Paper AI In 2024

Summarize research paper AI process with these 28 best tools hand-picked for 2024. Enhance your productivity and efficiency today.

Table of Contents

What Is An AI Research Paper Summarizer?

Unriddle allows you to read faster and write better, related reading, can ai summarize a document, 28 best tools to summarize research paper ai, 1. unriddle, 3. tenorshare ai, 4. scholarcy, 9. tldr this, 10. quillbot, 12. scalenut, 14. hypotenuse ai, 15. resoomer, 17. summarize bot, 18. anyword ai, 20. paraphraser.io, 21. simplified, 22. edrawmind, 23. ibm watson discovery, 24. scispacy, 25. contentbot, 26. semantic scholar, 27. shortly ai, 28. humata ai, how to choose an ai research paper summarizer, file compatibility, exportability, user interface and ease of use, customization options, can chatgpt summarize articles, the bottom line - try unriddle today, read faster & write better with unriddle for free today, simplifying complex topics, enhancing writing efficiency, facilitating collaboration with unriddle's workspace, unriddle: a versatile ai-powered platform.

• Unriddle generates an AI assistant on top of any document so you can quickly find, summarize and understand info. No more endless skimming.
• Unriddle understands the meaning behind your writing and automatically links you to relevant things you’ve read and written about in the past.
• Highlight text and Unriddle will show you the most relevant sources from your library using AI. Never lose a citation again.
• Generate text with AI autocomplete to improve and expand your writing, with all suggestions based on the context of what you're working on.
• Credible Sources For Research
• How To Find Sources For A Research Paper
• Literature Search Strategy
• How To Find Research Papers

• Unriddle generates an AI assistant on top of any document so you can quickly find, summarize, and understand info. No more endless skimming.
• Highlight text, and Unriddle will show you the most relevant sources from your library using AI. Never lose a citation again.

• One of the key features of Unriddle is its ability to generate an AI assistant on top of any document, allowing users to quickly find, summarize, and understand information.
• Unriddle uses AI to understand the meaning behind the user's writing and automatically links them to relevant things they have read and written about in the past.
• By highlighting text, Unriddle can show users the most relevant sources from their library using AI, ensuring they never lose a citation again.
• Unriddle offers the ability to generate text with AI autocomplete to improve and expand users' writing, with all suggestions based on the context of what they are working on.
• Unriddle also features a collaborative workspace where everyone can contribute and chat with the same documents in real-time.

• What Makes A Source Credible?
• Can Chatgpt Summarize A Pdf
• Ai Research Tools
• Can Chat Gpt Read A Pdf
• How To Summarize A Research Article
• How To Read A Scientific Paper
• How To Read A Research Paper
• How To Upload Pdf To Chatgpt
• How To Use Chatgpt To Summarize An Article
• How To Get Chat Gpt To Read A Pdf
• Ai Literature Review
• Analyse Research Paper
• How To Use Chat Gpt For Research
• Can Chat Gpt Summarize Text
• How To Read A Scholarly Article
• Ask Your Pdf Chatgpt
• How To Read A Research Paper Quickly
• Ai That Reads Pdf And Answers Questions
• Ai That Reads Pdf
• Pdf Reader Extension
• Pdf Summarizer Ai
• Chatpdf Alternative
• Scholarcy Alternative
• Ai Pdf Analyzer

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Research paper summarizer | An overview of the best AI summarizers

Table of Contents

In scholarly publishing, keeping abreast of the latest research findings and breakthroughs is vital. However, with the ever-expanding scope of scientific knowledge, reading and understanding research article is becoming a hard and time-consuming task.

Plus, the prevalent use of acronyms, jargon, and complex terminologies in research papers is impeding scientific engagement. Therefore, affecting the researcher’s reading interest and perceived understanding of research articles.

In light of this, a recent study from Science Direct enunciated that “automatic summarization of scientific articles helps students speed up their investigation process”. And this underscores the importance of AI tools like research paper summarizers.

This article enunciates the role and advantages of AI summarizers in enhancing reading comprehension.

What is a Research Paper Summarizer?

Research paper summarizer is an AI-powered article summarizer tool designed to condense extensive academic papers into concise summaries.

These summaries capture the critical points, key findings, and main arguments of a research article and represent them in the most succinct way possible. As a result, researchers quickly grasp the scope of the research paper without spending much time.

What are the benefits of research paper summarizers?

AI summarizers or research paper summarizers are a revolutionary tool that is changing the conventional reading method of scholarly publishing. While the inception of research paper summarizers may seem like a radical shift. By embracing this technology aid, the academic community can manage the challenge of reading comprehension.

Here's where AI summarizers steps in:

Saves you plenty of time

Research paper summarizers offer a streamlined path to the key points, insights, and findings of a paper, saving researchers hours of reading and sifting through vast information.

Imagine the productivity boost when you can certainly review ten papers in the time it used to take to read just one.

Managing information overload

Research paper summarizers combat information overload by presenting the most critical information from a paper instantly. This enables researchers to quickly identify whether key information in a paper is relevant to their work, while saving time from diving into lengthy papers that may not be directly related to their domain or research area of interest.

Improved accessibility for both academicians and non-academicians

Technical research papers are difficult to understand by non-academicians due to scientific jargon and complexity. However, these AI summarization tools make research more inclusive and understandable to non-scientific users, facilitating enhanced knowledge dissemination to users.

Extracts key findings in seconds

With PDF summarizers or AI summarizers, researchers can get critical findings of the research articles with a single click. These AI summarizers pull out the main key points of a long article efficiently, ensuring readers quickly grasp the essence of the study.

For example, Scholarcy, an AI-based tool, creates a summary-length flashcard of research papers enlisting the key insights, references, and figures. This way, it helps you speed-read the article consuming less time.

Cross-disciplinary insights

Traditional research papers are often confined within disciplinary boundaries, making it challenging for researchers to explore ideas beyond their domain or area of interest. Research paper summarizers, however, assist them in comprehending interdisciplinary insights by summarizing the paper in the simplest way possible.

That way, researchers can easily understand and summarize studies from diverse fields, fostering scientific innovation and novel perspectives.

Also read: Top online tools to boost your academic performance

Top 5 AI summarizers — For summarizing research papers

Scispace copilot.

SciSpace Copilot, an AI tool helps researchers understand research papers by summarizing every bit of the highlighted information. You just have to select the text portion and click on summarize, it generates a summary of the text in seconds. Once you have the summaries, it also allows you to ask follow-up questions if you have. Unlike other tools, it is not only limited to generating summaries, it also assists you with explaining math, tables, figures, and equations.

• Summarizes and explains text, math, tables, and equations.
• Get summaries in multiple languages. You can interact with the paper in 70+ languages
• It acts as a Chatbot for your paper. You can ask any questions relevant to your paper and it explains it to you
• The answers are reliable as they’re backed by citations.
• Sometimes, it shows repetitive responses

Scholarly is an online article summarizer tool that assists students and researchers in quickly summarizing and comprehending research articles, book chapters, reports, and other documents. The tool simplifies difficult information into digestible chunks, gives context to important sentences, highlights important passages, and enables users to quickly save or export summaries as word documents for later use.

For students and researchers who need to keep up with their academic reading and writing, Scholarcy is a great resource. This AI summarizing tool highlights important facts and results while breaking down intricate information into interactive flashcards. It highlights key points and generate links to open-access versions of cited sources. Any device can read, share, and annotate these flashcards, making it simple to understand the research papers.

• Provides key insights of a lengthy research paper in multiple summary flashcards
• Save your flashcards in a dedicated Scholarcy library
• Free Edge and Chrome extension
• Only 3 free summary flashcards per day
• Glitchy interface
• Need a subscription to access advanced features

TLDR this is an AI-powered tool quickly summarizes any research paper, essay , document, report, and others in just a click. You can get the article summary in two different types — short and brief summary (actual TLDR) or long and detailed summary based on your requirements. All you have to do is, enter the website URL or paste the link to the content and generate a summary

• Generate 10 free summaries at no cost
• The interface is pretty straightforward
• It also tells you if the summary sounds AI-generated or human-like
• The quality of the summary has to be improved as there is essentially no difference between short and long summaries
• Output throws a few sentences from the original text and doesn’t produce an authentic summary

Quillbot Summarizer

Quillbot is another AI summarizing tool designed to break down lengthy articles, papers, or documents into their essential points in an instant and easy way. It works similarly to its paraphrasing tool. By leveraging the power of AI and NLP models, Quillbot online summarizer ensures that the core information is summarized without losing the original context.

Users can benefit from two different AI modes, choosing between "Key Sentences" or "Paragraph Mode" summaries. This summarization tool offers unlimited usage and is 100% free, making it an invaluable asset for researchers and writers alike.

• Availability of different modes of summarization based on the type and length of the content
• You can also paraphrase the obtained summary for a better understanding
• The free version comes with a word limit of 1200 words
• It's observed that the accuracy goes off the line sometimes

Amidst these dedicated AI summarizers, let's not forget ChatGPT. While not exclusively a research paper summarizer, ChatGPT can be an effective tool for summarizing long articles , books, news articles, research articles, reports, and more. By feeding it with the text you wish to summarize, you can get a tailored summary in real time.

• Completely free to use
• Users can clarify, refine, or request more detailed summaries interactively.
• Can produce summaries of varying lengths based on user requirements.
• Might occasionally provide less accurate summaries for very niche topics
• The summaries can sometimes be more verbose

Wrapping up!

While the journey of research paper summarizers has seen massive amounts of growth and adaptation, it's also evident that challenges still exist in terms of accuracy. However, with the rapid pace of technological advancements and the solutions emerging in response to these changes, the future looks promising. These tools will become even more intuitive, accurate, and user-centric, further bridging the gap between new scientific discoveries and their easy comprehension.

Although AI summarizers are valuable tools for quickly extracting essential information from lengthy research papers they should be only used as aids for initial comprehension rather than as substitutes for in-depth reading and analysis. The quality of the summary may vary, so it's essential to evaluate their accuracy for your specific needs.

You can explore all the above-listed tools and let us know which one helped you the most!

Good reads, curated just for you!

Best AI Tools for Research Paper Writing

How To Write A Research Summary

AI tools for research: Revolutionize your work with top research assistants

5 literature review tools to ace your reseach (+2 bonus tools)

ChatPDF vs. SciSpace Copilot: AI tools to chat with your PDF

Citation Machine Alternatives — A comparison of top citation generator tools 2023

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Research Assistant

Ai-powered research summarizer.

• Summarize academic papers: Quickly understand the main points of research papers without reading the entire document.
• Extract insights from reports: Identify key findings and trends from industry reports, surveys, or reviews.
• Prepare for presentations: Create concise summaries of research materials to include in your presentations or talking points.
• Enhance your understanding: Improve your comprehension of complex subjects by summarizing the main ideas and insights.
• Save time: Reduce the time spent on reading lengthy research documents by focusing on the most important points.

New & Trending Tools

Ai quote generator, notes generator ai, ai writing ideas.

Analyze research papers at superhuman speed

Search for research papers, get one sentence abstract summaries, select relevant papers and search for more like them, extract details from papers into an organized table.

Find themes and concepts across many papers

Don't just take our word for it.

Tons of features to speed up your research

Upload your own pdfs, orient with a quick summary, view sources for every answer, ask questions to papers, research for the machine intelligence age, pick a plan that's right for you, get in touch, enterprise and institutions, custom pricing, common questions. great answers., how do researchers use elicit.

Over 2 million researchers have used Elicit. Researchers commonly use Elicit to:

• Speed up literature review
• Find papers they couldn’t find elsewhere
• Automate systematic reviews and meta-analyses
• Learn about a new domain

Elicit tends to work best for empirical domains that involve experiments and concrete results. This type of research is common in biomedicine and machine learning.

What is Elicit not a good fit for?

Elicit does not currently answer questions or surface information that is not written about in an academic paper. It tends to work less well for identifying facts (e.g. “How many cars were sold in Malaysia last year?”) and theoretical or non-empirical domains.

What types of data can Elicit search over?

Elicit searches across 125 million academic papers from the Semantic Scholar corpus, which covers all academic disciplines. When you extract data from papers in Elicit, Elicit will use the full text if available or the abstract if not.

How accurate are the answers in Elicit?

A good rule of thumb is to assume that around 90% of the information you see in Elicit is accurate. While we do our best to increase accuracy without skyrocketing costs, it’s very important for you to check the work in Elicit closely. We try to make this easier for you by identifying all of the sources for information generated with language models.

What is Elicit Plus?

Elicit Plus is Elicit's subscription offering, which comes with a set of features, as well as monthly credits. On Elicit Plus, you may use up to 12,000 credits a month. Unused monthly credits do not carry forward into the next month. Plus subscriptions auto-renew every month.

What are credits?

Elicit uses a credit system to pay for the costs of running our app. When you run workflows and add columns to tables it will cost you credits. When you sign up you get 5,000 credits to use. Once those run out, you'll need to subscribe to Elicit Plus to get more. Credits are non-transferable.

How can you get in contact with the team?

Please email us at [email protected] or post in our Slack community if you have feedback or general comments! We log and incorporate all user comments. If you have a problem, please email [email protected] and we will try to help you as soon as possible.

What happens to papers uploaded to Elicit?

When you upload papers to analyze in Elicit, those papers will remain private to you and will not be shared with anyone else.

How accurate is Elicit?

Training our models on specific tasks, searching over academic papers, making it easy to double-check answers, save time, think more. try elicit for free..

The best AI tools for research papers and academic research (Literature review, grants, PDFs and more)

As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change. 

Make no mistake, AI is here to stay!

Instead of tirelessly scrolling through hundreds of PDFs, a powerful AI tool comes to your rescue, summarizing key information in your research papers. Instead of manually combing through citations and conducting literature reviews, an AI research assistant proficiently handles these tasks.

These aren’t futuristic dreams, but today’s reality. Welcome to the transformative world of AI-powered research tools!

The influence of AI in scientific and academic research is an exciting development, opening the doors to more efficient, comprehensive, and rigorous exploration.

This blog post will dive deeper into these tools, providing a detailed review of how AI is revolutionizing academic research. We’ll look at the tools that can make your literature review process less tedious, your search for relevant papers more precise, and your overall research process more efficient and fruitful.

I know that I wish these were around during my time in academia. It can be quite confronting when trying to work out what ones you should and shouldn’t use. A new one seems to be coming out every day!

Here is everything you need to know about AI for academic research and the ones I have personally trialed on my Youtube channel.

Best ChatGPT interface – Chat with PDFs/websites and more

I get more out of ChatGPT with HeyGPT . It can do things that ChatGPT cannot which makes it really valuable for researchers.

Use your own OpenAI API key ( h e re ). No login required. Access ChatGPT anytime, including peak periods. Faster response time. Unlock advanced functionalities with HeyGPT Ultra for a one-time lifetime subscription

AI literature search and mapping – best AI tools for a literature review – elicit and more

Harnessing AI tools for literature reviews and mapping brings a new level of efficiency and precision to academic research. No longer do you have to spend hours looking in obscure research databases to find what you need!

AI-powered tools like Semantic Scholar and elicit.org use sophisticated search engines to quickly identify relevant papers.

They can mine key information from countless PDFs, drastically reducing research time. You can even search with semantic questions, rather than having to deal with key words etc.

With AI as your research assistant, you can navigate the vast sea of scientific research with ease, uncovering citations and focusing on academic writing. It’s a revolutionary way to take on literature reviews.

• Elicit –  https://elicit.org
• Supersymmetry.ai: https://www.supersymmetry.ai
• Semantic Scholar: https://www.semanticscholar.org
• Connected Papers –  https://www.connectedpapers.com/
• Research rabbit – https://www.researchrabbit.ai/
• Laser AI –  https://laser.ai/
• Litmaps –  https://www.litmaps.com
• Inciteful –  https://inciteful.xyz/
• Scite –  https://scite.ai/
• System –  https://www.system.com

If you like AI tools you may want to check out this article:

• How to get ChatGPT to write an essay [The prompts you need]

AI-powered research tools and AI for academic research

AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research. 

These AI-powered tools can efficiently summarize PDFs, extract key information, and perform AI-powered searches, and much more. Some are even working towards adding your own data base of files to ask questions from. 

Tools like scite even analyze citations in depth, while AI models like ChatGPT elicit new perspectives.

The result? The research process, previously a grueling endeavor, becomes significantly streamlined, offering you time for deeper exploration and understanding. Say goodbye to traditional struggles, and hello to your new AI research assistant!

• Bit AI –  https://bit.ai/
• Consensus –  https://consensus.app/
• Exper AI –  https://www.experai.com/
• Hey Science (in development) –  https://www.heyscience.ai/
• Iris AI –  https://iris.ai/
• PapersGPT (currently in development) –  https://jessezhang.org/llmdemo
• Research Buddy –  https://researchbuddy.app/
• Mirror Think – https://mirrorthink.ai

AI for reading peer-reviewed papers easily

Using AI tools like Explain paper and Humata can significantly enhance your engagement with peer-reviewed papers. I always used to skip over the details of the papers because I had reached saturation point with the information coming in. 

These AI-powered research tools provide succinct summaries, saving you from sifting through extensive PDFs – no more boring nights trying to figure out which papers are the most important ones for you to read!

They not only facilitate efficient literature reviews by presenting key information, but also find overlooked insights.

With AI, deciphering complex citations and accelerating research has never been easier.

• Open Read –  https://www.openread.academy
• Chat PDF – https://www.chatpdf.com
• Explain Paper – https://www.explainpaper.com
• Humata – https://www.humata.ai/
• Lateral AI –  https://www.lateral.io/
• Paper Brain –  https://www.paperbrain.study/
• Scholarcy – https://www.scholarcy.com/
• SciSpace Copilot –  https://typeset.io/
• Unriddle – https://www.unriddle.ai/
• Sharly.ai – https://www.sharly.ai/

AI for scientific writing and research papers

In the ever-evolving realm of academic research, AI tools are increasingly taking center stage.

Enter Paper Wizard, Jenny.AI, and Wisio – these groundbreaking platforms are set to revolutionize the way we approach scientific writing.

Together, these AI tools are pioneering a new era of efficient, streamlined scientific writing.

• Paper Wizard –  https://paperwizard.ai/
• Jenny.AI https://jenni.ai/ (20% off with code ANDY20)
• Wisio – https://www.wisio.app

AI academic editing tools

In the realm of scientific writing and editing, artificial intelligence (AI) tools are making a world of difference, offering precision and efficiency like never before. Consider tools such as Paper Pal, Writefull, and Trinka.

Together, these tools usher in a new era of scientific writing, where AI is your dedicated partner in the quest for impeccable composition.

• Paper Pal –  https://paperpal.com/
• Writefull –  https://www.writefull.com/
• Trinka –  https://www.trinka.ai/

AI tools for grant writing

In the challenging realm of science grant writing, two innovative AI tools are making waves: Granted AI and Grantable.

These platforms are game-changers, leveraging the power of artificial intelligence to streamline and enhance the grant application process.

Granted AI, an intelligent tool, uses AI algorithms to simplify the process of finding, applying, and managing grants. Meanwhile, Grantable offers a platform that automates and organizes grant application processes, making it easier than ever to secure funding.

Together, these tools are transforming the way we approach grant writing, using the power of AI to turn a complex, often arduous task into a more manageable, efficient, and successful endeavor.

• Granted AI – https://grantedai.com/
• Grantable – https://grantable.co/

Free AI research tools

There are many different tools online that are emerging for researchers to be able to streamline their research processes. There’s no need for convience to come at a massive cost and break the bank.

The best free ones at time of writing are:

• Elicit – https://elicit.org
• Connected Papers – https://www.connectedpapers.com/
• Litmaps – https://www.litmaps.com ( 10% off Pro subscription using the code “STAPLETON” )
• Consensus – https://consensus.app/

Wrapping up

The integration of artificial intelligence in the world of academic research is nothing short of revolutionary.

With the array of AI tools we’ve explored today – from research and mapping, literature review, peer-reviewed papers reading, scientific writing, to academic editing and grant writing – the landscape of research is significantly transformed.

The advantages that AI-powered research tools bring to the table – efficiency, precision, time saving, and a more streamlined process – cannot be overstated.

These AI research tools aren’t just about convenience; they are transforming the way we conduct and comprehend research.

They liberate researchers from the clutches of tedium and overwhelm, allowing for more space for deep exploration, innovative thinking, and in-depth comprehension.

Whether you’re an experienced academic researcher or a student just starting out, these tools provide indispensable aid in your research journey.

And with a suite of free AI tools also available, there is no reason to not explore and embrace this AI revolution in academic research.

We are on the precipice of a new era of academic research, one where AI and human ingenuity work in tandem for richer, more profound scientific exploration. The future of research is here, and it is smart, efficient, and AI-powered.

Before we get too excited however, let us remember that AI tools are meant to be our assistants, not our masters. As we engage with these advanced technologies, let’s not lose sight of the human intellect, intuition, and imagination that form the heart of all meaningful research. Happy researching!

Thank you to Ivan Aguilar – Ph.D. Student at SFU (Simon Fraser University), for starting this list for me!

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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10 AI Tools for Research Paper Summarization

In this blog post, we will explore the 10 best AI-Powered tools for research paper summarization that can enhance your efficiency and productivity in the academic realm.

In the world of academia, academic papers play a crucial role in disseminating knowledge and advancing scientific understanding.

However, with the ever-increasing volume of scientific literatures being published, it has become challenging for researchers to keep up with the vast amount of information available. This is where AI tools for research paper summarization come to the rescue.

The AI tools let you summarize a scientific article in a few sentences or paragraphs, highlight the main points, findings and contributions of the paper.

Best AI Tools for Research Paper Summarization

In this section, we will explore ten remarkable AI-based summarization tools that can simplify the process of summarizing academic papers.

Here are some of the best AI-powered tools for research paper summarization:

#1. SciSummary

SciSummary (short for science summary) is an AI-driven tool designed and developed to provide comprehensive summaries of scientific papers.

The online handy tool uses GPT-3.5 and GPT-4 models to cater summaries of any scientific articles.

In addition to that you can use the free online summarizing tool by sending an email with text, a link, or even attaching a PDF of the article you want to summarize. Within minutes, you will receive a summary in your inbox.

It makes it easy to stay up-to-date with latest scientific breakthroughs and research findings, without having to spend hours reading through long and complicated  articles.

#2. SummarizeBot

SummarizeBot is an AI and blockchain-powered tool that can summarize any kind of information for you, including weblinks, documents, images, and more.

The web application uses AI technology to extract the most relevant information from the text and present it in a concise and easy-to-understand format.

Moreover, the tool can summarize a wide range of formats, including text, PDFs, images, and more.

You can customize the length of the summary, so you can get just the information you need.

This summary generator tool is free to use, with no word limits or daily limits.

#3. Copernic Summarizer

Copernic Summarizer is a software application that creates concise document summaries of any file or web page.

It uses sophisticated statistical and linguistic algorithm s to pinpoint the key concepts and extract the most relevant sentences, resulting in a web site or document summary that is a shorter, condensed version of the original text.

In order to use Copernic Summarizer app, simply open the paper that you want to summarize. Then, click on the “Summarize” button.

Copernic Summarizer will then analyze the paper and create a summary. The summary will be displayed in a new window.

Once you are satisfied with the summary, you can copy it to the clipboard or save it to a file.

It is a useful tool for anyone who needs to quickly and easily summarize documents. It is especially useful for researchers who need to read and understand large amounts of information.

Copernic Summarizer lets you summarize a variety of file types, including PDFs, Word documents, text files, and web pages.

Additionally, it can also be used to summarize documents in a variety of languages, including English, French, German, and Spanish.

This AI-based tool  is available in both free and paid versions.

#4. Resoomer

Resoomer is an AI-powered online text summarizer tool that can create concise and informative summaries of long documents in a variety of languages.

The Resoomer is available in both a free and paid version.

To use Resoomer, simply paste the content of the paper that you want to summarize into the text box on the Resoomer website. Then, select the language of the text and the desired length of the summary.

Resoomer will then generate a summary and display it in a new window.

It is also integrated with a number of other research tools, such as Google Scholar and Mendeley.

#5. Iris.AI

Iris.AI  is an advanced AI-based summarization tool that can effectively condense lengthy scholarly paper into concise summaries.

Iris.AI analyzes the content of research papers or other text inputs and generates abridged summaries that capture the essential information.

With Iris.AI as a summarizer, users can obtain the key points, main ideas, and important findings of a document without having to read the entire text.

This saves time and enables researchers to quickly grasp the core content and extract the necessary insights.

Whether it is a research papers, articles, or other written materials, Iris.AI simplifies the information overload and facilitates efficient information consumption.

Researchers can streamline their reading and research workflow s, enhancing productivity and enabling them to stay up-to-date with a vast amount of information in a more time-efficient manner.

#6.  Scholarcy

Scholarcy is an advanced AI-powered tool designed and developed to simplify the process of reading, understanding, and summarizing scientific research papers.

By leveraging AI technology, Scholarcy helps users overcome information overload, save time, and enhance productivity in the research process.

The cutting-edge tool analyzes the content of research papers and generates concise summaries.

Besides, it enables researchers to quickly grasp the key points and main ideas without having to read the entire paper.

Utilizing advanced algorithm s the tool allows you to identify and extract essential information, including key concepts, methodology, and results, providing users with comprehensive summaries.

Its summarization process ensures that the generated summaries are coherent and informative, providing researchers with a clear overview of the original paper.

In a previous post titled “ how to summarize an academic research article (Academic) ” I extensively covered the capabilities and features of the Scholarcy tool.

#7. Paper Digest

Paper Digest is an AI-driven platform that helps researchers stay up-to-date with the latest research.

It is a short summary of an academic article that highlights the main points and core ideas of the paper.

It can help readers to quickly grasp the paper’s contribution and relevance without reading the full text.

With Paper Digest, users can input a research paper or provide a URL, and the tool automatically generates a summary that captures the main findings, methodologies, and key points of the paper.

This allows researchers to quickly grasp the essence of a paper without reading it in its entirety and simplify the scholarly work.

Paper Digest’s summarization process ensures that the generated summaries are accurate, coherent, and provide a comprehensive overview of the original paper.

By condensing lengthy research papers into concise summaries, Paper Digest saves time and enhances the efficiency of literature review and information extraction.

In my earlier post how to generate an automatic summary of research paper , I already elaborately discussed Papper Digest AI-powered tool.

#8. Lynx AI

Lynx AI of SciLynk is also a great AI tool for research paper summarization. It is a free online tool that uses AI to identify the key points of a research paper and generate a summary in a few minutes.

Besides, Lynx AI Summarization offers a number of customization options, such as the ability to select the level of detail and the language of the summary.

Your personal AI research assistant that will summarize search results and key finidings to distill complex concepts at a glance.

As I already mentioned this cutting edge tool uses AI technology to identify the key points of a research paper. This means that the summary will be accurate and relevant to the academic paper.

Lynx AI can generate summaries in a variety of lengths.

The summarizing tool offers a number of customization options. You can control the level of detail and the language of the summary.

Genei is an AI-powered summarization tool that helps you research faster by automatically summarizing background reading, producing articles and reports faster.

Moreover, you can use Genei to import, view, summarize and analyze PDFs and webpages.

Genei seamlessly integrates with popular platforms such as Google Drive, Microsoft OneDrive, and Dropbox.

It also provides features such as notepad and annotation capabilities, citation management and reference generator, multi-document summarization, and more.

The AI-driven tool is revolutionizing the research landscape by offering intelligent summarization.

With its ability to  summarize, Genei significantly enhances research efficiency and productivity.

#10. TLDR This

TLDR This  is a free online tool that can summarize research papers in a variety of lengths. The tool uses AI to identify the key points of a paper and generate a summary that is easy to read and understand.

The TLDR tool enables you to condense any text into a brief, easily understandable format, allowing you to escape the burden of excessive information.

TLDR This also offers a number of customization options, such as the ability to select the level of detail and the language of the summary.

To obtain a summary of a scholarly paper, either enter the article URL or paste the text into the provided field.

Drawbacks and Constraints of AI Tools for Research Paper Summarization

Here are the drawbacks and constraints of AI tools for research paper summarization:

• Lack of contextual understanding and nuanced interpretation.
• Difficulty in handling complex technical language and domain-specific terms.
• Potential introduction of biases or subjective interpretations.
• Insufficient domain expertise to capture intricate details accurately.
• Challenges in comprehending mathematical equations, diagrams, or visual content.
• Incomplete coverage of key points, findings, or supporting evidence.

Final Thought

In this blog post, we have introduced you to some of the best AI tools for research paper summarization that helps you read and understand scientific article faster.

These tools provide summaries of key sections of the research paper, highlight the main points, findings and contributions of the paper, demystify technical language, contextualize the research and support literature review s.

However, these tools are not perfect and may have limitations or errors in their outputs. Therefore, you should always verify the accuracy and quality of the summaries before using them for your own purposes.

We hope that this blog post has been useful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!

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SciSummary: Ai Tool to Summarize Complex Scientific Articles

SciSummary is an AI-powered software that simplifies complex scientific articles into concise and easily digestible summaries. It utilizes a custom-tuned GPT-3 model to distill lengthy research papers into manageable nuggets, saving students, scientists, and enthusiasts valuable time. While currently lacking integrations with other software, SciSummary’s focus on data accuracy and quality control positions it as a game-changer in unlocking scientific knowledge efficiently.

Table of Contents

Understanding scisummary.

SciSummary is a revolutionary AI-driven software that transforms how we consume scientific articles. It utilizes cutting-edge technology to provide users with condensed, thorough summaries of intricate and lengthy articles.

You can think of it like your personal science digest, turning pages upon pages of complex information into more manageable nuggets that are easier to comprehend.

Launched in 2023 and based out of the United States, this tool has proven incredibly effective for students, scientists, and anyone interested in keeping up-to-date with current research without having to sift through dense language or complicated concepts.

Purpose And Functionality

SciSummary’s innovative purpose is to simplify scientific articles’ complex world and save users valuable time. Leveraging a custom-tuned GPT model, this groundbreaking AI-driven tool distills dense scientific knowledge into concise, easy-to-understand summaries.

In terms of functionality, it couldn’t be easier to use. To obtain a summary, users merely need to email text, links, or PDF attachments containing their chosen scholarly pieces – no more sifting through complex jargon-filled documents! Within minutes they receive an accurate summary directly in their inbox.

It’s worth noting that while Sci Summary provides outstanding convenience free of cost at present, with user-rated results ensuring data accuracy and quality control – it does not yet offer any integration options within other software frameworks, which could be seen as an area ripe for future development.

The Power Of SciSummary In Unlocking Scientific Knowledge

SciSummary harnesses the power of artificial intelligence to summarize cutting-edge scientific articles, providing users with concise and easily digestible summaries. By enabling busy individuals like students, scientists, and enthusiasts to stay up-to-date with the latest research without spending hours reading lengthy papers, SciSummary unlocks a wealth of scientific knowledge at their fingertips.

Summarization Of Cutting-edge Scientific Articles

SciSummary stands as a beacon of innovation in scientific literature, cutting through advanced research papers’ complexities and jargon-laden text to deliver concise and comprehensible summaries.

This game-changing AI tool leverages a custom-tuned GPT-3 model to rapidly parse even the densest scientific articles.

Take, for example, a theoretical physicist immersed in up-to-the-minute research on string theory or quantum gravity. These are undeniably complex topics with publications that could take hours to fully comprehend.

Instead of manually sifting through volumes of convoluted research papers, they email the PDF file directly to SciSummary.

The accessibility offered by SciSummary also aids students grappling with intricate concepts during their academic pursuits—a molecular biology student struggling with new cell division studies would greatly benefit from quick overviews delivered straight into their inbox! Harnessing SciSummary makes absorbing innovative ideas and breakthroughs faster than ever before, unlocking scientific knowledge for all curious minds.

Role Of Artificial Intelligence

Artificial Intelligence (AI) plays a crucial role in unlocking scientific knowledge through the power of SciSummary. Using advanced AI algorithms, SciSummary can summarize cutting-edge scientific articles within minutes, making it easier for users to understand complex research papers.

With the help of AI, SciSummary can process vast amounts of scientific information and distill it into easily digestible summaries. This allows researchers, students, and scientists to stay up-to-date with the latest field developments without spending hours reading lengthy research papers.

For example, imagine a Ph.D. student who needs to review dozens of articles as part of their literature review.

The integration of AI also enables SciSummary to continuously improve its performance over time by learning from user feedback and refining its summarization algorithms. This ensures that users receive accurate and relevant summaries aligning with their needs.

In conclusion, AI is revolutionizing how we access scientific knowledge by enabling tools like SciSummary to efficiently streamline the process of understanding complex research papers.

Key Features Of SciSummary

SciSummary offers an AI Writing Assistant, making it easy to quickly input and summarize scientific articles.

AI Writing Assistant

The AI Writing Assistant is one of the key features of SciSummary, an AI-powered software that aims to summarize scientific articles. With this feature, users can enjoy the benefits of advanced natural language processing (NLP) technology to enhance their writing experience.

The AI Writing Assistant provides various tools and functionalities such as a style editor, sentence formatting, sentence rephraser, engagement metrics, plagiarism check, tone checker, grammar check, autocorrect, and more.

This means that not only does it help summarize scientific articles, but it also assists users in improving their overall writing skills and ensuring accuracy in their work.

Whether you’re a student working on research papers or a scientist publishing academic articles, the AI Writing Assistant supports you every step of the way.

Integrations

SciSummary currently does not have any integrations with other software. However, the platform offers a range of features and options that can enhance the user experience. Here are some key features and functionalities of SciSummary:

• Chatbot functionality: Users can interact with an AI-powered chatbot to navigate the software and obtain assistance summarizing scientific articles.
• Image recognition: SciSummary utilizes advanced algorithms to analyze images and extract relevant information, allowing users to incorporate visual content into their summaries.
• Machine learning: The software leverages machine learning techniques to continuously improve its summarization capabilities, ensuring accurate and concise summaries.
• Natural language processing (NLP): With NLP technologies, SciSummary can understand the complex scientific text and generate summaries capturing essential information.
• Predictive analytics: SciSummary uses predictive analytics to anticipate user preferences and provide tailored summary results by analyzing previous user interactions and feedback.

While no current integrations exist with external software, SciSummary aims to provide a comprehensive solution for summarizing scientific articles. Its customizable AI model and various built-in functionalities allow users to streamline their reading process and gain quick insights from lengthy publications.

Good to know:

• SciSummary is an AI-driven summarization tool designed to simplify the understanding of scientific articles.
• The platform utilizes a custom-tuned GPT-3 model and is currently in public beta.
• At present, SciSummary does not have any integrations with other software.

Reviews And Recommendations For SciSummary

SciSummary, the AI-powered software that enables the summarization of scientific articles, has been gaining attention and generating buzz in the scientific community.

With its free version available during its public beta stage, SciSummary offers a cost-effective solution for optimizing their time while staying up-to-date with the latest scientific advancements.

Despite not having any integrations, SciSummary’s focus on providing high-quality summaries ensures data accuracy while offering convenience to its users.

Although relatively new (founded in 2023), SciSummary is already positioned as a key player in automated article summarization.

In conclusion, SciSummary represents a powerful tool capable of transforming how we navigate vast amounts of scientific literature. By condensing cutting-edge research into concise summaries using advanced natural language processing algorithms, it empowers users by giving them access to valuable insights that can be leveraged for various purposes—from student assignments to groundbreaking discoveries.

Comparing SciSummary With Alternatives

While SciSummary is a relatively new player in the field, it is a unique offering of AI-powered summarization of scientific articles sets it apart. It fills a necessary gap in the market, catering to the needs of busy students, scientists, and enthusiasts who crave to stay updated with scientific advancements but struggle with time constraints. Although the software lacks integrations with other platforms, its standalone functionality is promising. As SciSummary moves forward, user reviews are yet to be seen, but the potential for this tool is undeniable.

In conclusion, SciSummary is revolutionizing how we access and understand scientific knowledge. Its AI-powered software allows users to quickly and accurately summarize complex scientific articles, saving valuable time for students, scientists, and enthusiasts alike.

The tool’s free version allows for easy access to this powerful technology. While it has no integrations or user reviews, SciSummary presents a promising solution for unlocking scientific knowledge through efficient summarization.

As we continue to explore the possibilities of artificial intelligence in research and education, tools like SciSummary will play a crucial role in democratizing information and fostering innovation.

What is SciSummary, and how does it unlock scientific knowledge?

SciSummary is a tool that summarizes scientific research articles, making the information more accessible and digestible for readers. By condensing complex studies into concise summaries, SciSummary enables individuals to quickly understand key findings without reading lengthy technical papers.

How accurate are the summaries provided by SciSummary?

SciSummary aims to provide accurate and reliable summaries of scientific research articles. The platform utilizes advanced algorithms and natural language processing techniques to ensure that the key information from the original paper is accurately conveyed concisely.

Can I trust the information provided by SciSummary as I would trust reading the full research article?

While SciSummary strives to present accurate information, it’s important to note that nuances or additional details in the full research article may not be captured in the summary. For a comprehensive understanding, researchers should consult primary sources directly.

How can scientists benefit from using SciSummary?

Scientists can save significant time and effort by using SciSummary as a starting point for their literature reviews and staying updated on recent publications within their field of study. The tool provides quick access to relevant insights, allowing researchers to identify relevant studies efficiently and facilitate collaboration among peers.

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Extract key information from research papers with our AI summarizer.

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Clutter free flashcards help you skim or diver deeper into the details and quickly jump between sections.

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Scholarcy’s ai summarization tool is designed to generate accurate, reliable article summaries..

Our summarizer tool is trained to identify key terms, claims, and findings in academic papers. These insights are turned into digestible Summary Flashcards.

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The knowledge extraction and summarization methods we use focus on accuracy. This ensures what you read is factually correct, and can always be traced back to the original source .

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It would normally take me 15mins – 1 hour to skim read the article but with Scholarcy I can do that in 5 minutes.

Scholarcy makes my life easier because it pulls out important information in the summary flashcard.

Scholarcy is clear and easy to navigate. It helps speed up the process of reading and understating papers.

Join over 400,000 people already saving time.

From a to z with scholarcy, generate flashcard summaries. discover more aha moments. get to point quicker..

Understand complex research. Jump between key concepts and sections.   Highlight text. Take notes.

Build a library of knowledge. Recall important info with ease. Organize, search, sort, edit.

Bring it all together. Export Flashcards in a range of formats. Transfer Flashcards into other apps.

Apply what you’ve learned. Compile your highlights, notes, references. Write that magnum opus 🤌

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Get unlimited summaries, advanced research and analysis features, and your own personalised collection with Scholarcy Library!

With Scholarcy Library you can import unlimited documents and generate summaries for all your course materials or collection of research papers.

Scholarcy Library offers additional features including access to millions of academic research papers, customizable summaries, direct import from Zotero and more.

Scholarcy lets you build and organise your summaries into a handy library that you can access from anywhere. Export from a range of options, including one-click bibliographies and even a literature matrix.

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SciSummary is an AI-powered tool that uses language models like GPT-3.5 and GPT-4 to automatically summarize lengthy scientific articles and research papers. It’s ideal for busy researchers, students, and anyone who needs to quickly digest complex academic content. SciSummary allows you to simply upload a document or provide a link and click a button to receive a summary. The tool offers a free plan with 10,000 words summarized per month, as well as paid plans starting at $4.99/month for those requiring higher volume usage.

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An AI helps you summarize the latest in AI

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The news: A new AI model for summarizing scientific literature can now assist researchers in wading through and identifying the latest cutting-edge papers they want to read. On November 16, the Allen Institute for Artificial Intelligence (AI2) rolled out the model onto its flagship product, Semantic Scholar , an AI-powered scientific paper search engine. It provides a one-sentence tl;dr (too long; didn’t read) summary under every computer science paper (for now) when users use the search function or go to an author’s page. The work was also accepted to the Empirical Methods for Natural Language Processing conference this week.

The context: In an era of information overload, using AI to summarize text has been a popular natural-language processing (NLP) problem. There are two general approaches to this task. One is called “extractive,” which seeks to find a sentence or set of sentences from the text verbatim that captures its essence. The other is called “abstractive,” which involves generating new sentences. While extractive techniques used to be more popular due to the limitations of NLP systems, advances in natural language generation in recent years have made the abstractive one a whole lot better.

How they did it: AI2’s abstractive model uses what’s known as a transformer—a type of neural network architecture first invented in 2017 that has since powered all of the major leaps in NLP, including OpenAI’s GPT-3 . The researchers first trained the transformer on a generic corpus of text to establish its baseline familiarity with the English language. This process is known as “pre-training” and is part of what makes transformers so powerful. They then fine-tuned the model—in other words, trained it further—on the specific task of summarization.

The fine-tuning data: The researchers first created a dataset called SciTldr, which contains roughly 5,400 pairs of scientific papers and corresponding single-sentence summaries. To find these high-quality summaries, they first went hunting for them on OpenReview, a public conference paper submission platform where researchers will often post their own one-sentence synopsis of their paper. This provided a couple thousand pairs. The researchers then hired annotators to summarize more papers by reading and further condensing the synopses that had already been written by peer reviewers.

To supplement these 5,400 pairs even further, the researchers compiled a second dataset of 20,000 pairs of scientific papers and their titles. The researchers intuited that because titles themselves are a form of summary, they would further help the model improve its results. This was confirmed through experimentation.

Extreme summarization: While many other research efforts have tackled the task of summarization, this one stands out for the level of compression it can achieve. The scientific papers included in the SciTldr dataset average 5,000 words. Their one-sentence summaries average 21. This means each paper is compressed on average to 238 times its size. The next best abstractive method is trained to compress scientific papers by an average of only 36.5 times. During testing, human reviewers also judged the model’s summaries to be more informative and accurate than previous methods.

Next steps: There are already a number of ways that AI2 is now working to improve their model in the short term, says Daniel Weld, a professor at the University of Washington and manager of the Semantic Scholar research group. For one, they plan to train the model to handle more than just computer science papers. For another, perhaps in part due to the training process, they’ve found that the tl;dr summaries sometimes overlap too much with the paper title, diminishing their overall utility. They plan to update the model’s training process to penalize such overlap so it learns to avoid repetition over time.

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• 20 March 2024

Is AI ready to mass-produce lay summaries of research articles?

• Kamal Nahas 0

Kamal Nahas is a freelance science journalist based in Oxford, UK

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Generative AI might be a powerful tool in making research more accessible for scientists and the broader public alike. Credit: Getty

Thinking back to the early days of her PhD programme, Esther Osarfo-Mensah recalls struggling to keep up with the literature. “Sometimes, the wording or the way the information is presented actually makes it quite a task to get through a paper,” says the biophysicist at University College London. Lay summaries could be a time-saving solution. Short synopses of research articles written in plain language could help readers to decide which papers to focus on — but they aren’t common in scientific publishing. Now, the buzz around artificial intelligence (AI) has pushed software engineers to develop platforms that can mass produce these synopses.

Scientists are drawn to AI tools because they excel at crafting text in accessible language, and they might even produce clearer lay summaries than those written by people. A study 1 released last year looked at lay summaries published in one journal and found that those created by people were less readable than were the original abstracts — potentially because some researchers struggle to replace jargon with plain language or to decide which facts to include when condensing the information into a few lines.

AI lay-summary platforms come in a variety of forms (see ‘AI lay-summary tools’). Some allow researchers to import a paper and generate a summary; others are built into web servers, such as the bioRxiv preprint database.

AI lay-summary tools

Several AI resources have been developed to help readers glean information about research articles quickly. They offer different perks. Here are a few examples and how they work:

- SciSummary: This tool parses the sections of a paper to extract the key points and then runs those through the general-purpose large language model GPT-3.5 to transform them into a short summary written in plain language. Max Heckel, the tool’s founder, says it incorporates multimedia into the summary, too: “If it determines that a particular section of the summary is relevant to a figure or table, it will actually show that table or figure in line.”

- Scholarcy: This technology takes a different approach. Its founder, Phil Gooch, based in London, says the tool was trained on 25,000 papers to identify sentences containing verb phrases such as “has been shown to” that often carry key information about the study. It then uses a mixture of custom and open-source large language models to paraphrase those sentences in plain text. “You can actually create ten different types of summaries,” he adds, including one that lays out how the paper is related to previous publications.

- SciSpace: This tool was trained on a repository of more than 280 million data sets, including papers that people had manually annotated, to extract key information from articles. It uses a mixture of proprietary fine-tuned models and GPT-3.5 to craft the summary, says the company’s chief executive, Saikiran Chandha, based in San Francisco, California. “A user can ask questions on top of these summaries to further dig into the paper,” he notes, adding that the company plans to develop audio summaries that people can tune into on the go.

Benefits and drawbacks

Mass-produced lay summaries could yield a trove of benefits. Beyond helping scientists to speed-read the literature, the synopses can be disseminated to people with different levels of expertise, including members of the public. Osarfo-Mensah adds that AI summaries might also aid people who struggle with English. “Some people hide behind jargon because they don’t necessarily feel comfortable trying to explain it,” she says, but AI could help them to rework technical phrases. Max Heckel is the founder of SciSummary, a company in Columbus, Ohio, that offers a tool that allows users to import a paper to be summarized. The tool can also translate summaries into other languages, and is gaining popularity in Indonesia and Turkey, he says, arguing that it could topple language barriers and make science more accessible.

Despite these strides, some scientists feel that improvements are needed before we can rely on AI to describe studies accurately.

Will Ratcliff, an evolutionary biologist at the Georgia Institute of Technology in Atlanta, argues that no tool can produce better text than can professional writers. Although researchers have different writing abilities, he invariably prefers reading scientific material produced by study authors over those generated by AI. “I like to see what the authors wrote. They put craft into it, and I find their abstract to be more informative,” he says.

Is ChatGPT making scientists hyper-productive? The highs and lows of using AI

Nana Mensah, a PhD student in computational biology at the Francis Crick Institute in London, adds that, unlike AI, people tend to craft a narrative when writing lay summaries, helping readers to understand the motivations behind each step of the study. He says, however, that one advantage of AI platforms is that they can write summaries at different reading levels, potentially broadening the audience. In his experience, however, these synopses might still include jargon that can confuse readers without specialist knowledge.

AI tools might even struggle to turn technical language into lay versions at all. Osarfo-Mensah works in biophysics, a field with many intricate parameters and equations. She found that an AI summary of one of her research articles excluded information from a whole section. If researchers were looking for a paper with those details and consulted the AI summary, they might abandon her paper and look for other work.

Andy Shepherd, scientific director at global technology company Envision Pharma Group in Horsham, UK, has in his spare time compared the performances of several AI tools to see how often they introduce blunders. He used eight text generators, including general ones and some that had been optimized to produce lay summaries. He then asked people with different backgrounds, such as health-care professionals and the public, to assess how clear, readable and useful lay summaries were for two papers.

“All of the platforms produced something that was coherent and read like a reasonable study, but a few of them introduced errors, and two of them actively reversed the conclusion of the paper,” he says. It’s easy for AI tools to make this mistake by, for instance, omitting the word ‘not’ in a sentence, he explains. Ratcliff cautions that AI summaries should be viewed as a tool’s “best guess” of what a paper is about, stressing that it can’t check facts.

Broader readership

The risk of AI summaries introducing errors is one concern among many. Another is that one benefit of such summaries — that they can help to share research more widely among the public — could also have drawbacks. The AI summaries posted alongside bioRxiv preprints, research articles that have yet to undergo peer review, are tailored to different levels of reader expertise, including that of the public. Osarfo-Mensah supports the effort to widen the reach of these works. “The public should feel more involved in science and feel like they have a stake in it, because at the end of the day, science isn’t done in a vacuum,” she says.

But others point out that this comes with the risk of making unreviewed and inaccurate research more accessible. Mensah says that academics “will be able to treat the article with the sort of caution that’s required”, but he isn’t sure that members of the public will always understand when a summary refers to unreviewed work. Lay summaries of preprints should come with a “hazard warning” informing the reader upfront that the material has yet to be reviewed, says Shepherd.

Why scientists trust AI too much — and what to do about it

“We agree entirely that preprints must be understood as not peer-reviewed when posted,” says John Inglis, co-founder of bioRxiv, who is based at Cold Spring Harbor Laboratory in New York. He notes that such a disclaimer can be found on the homepage of each preprint, and if a member of the public navigates to a preprint through a web search, they are first directed to the homepage displaying this disclaimer before they can access the summary. But the warning labels are not integrated into the summaries, so there is a risk that these could be shared on social media without the disclaimer. Inglis says bioRxiv is working with its partner ScienceCast, whose technology produces the synopses, on adding a note to each summary to negate this risk.

As is the case for many other nascent generative-AI technologies, humans are still working out the messaging that might be needed to ensure users are given adequate context. But if AI lay-summary tools can successfully mitigate these and other challenges, they might become a staple of scientific publishing.

doi: https://doi.org/10.1038/d41586-024-00865-4

Wen, J. & Yi, L. Scientometrics 128 , 5791–5800 (2023).

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A new use for AI: summarizing scientific research for seven-year-olds

Tl;dr papers shows ai’s potential to condense research.

By James Vincent , a senior reporter who has covered AI, robotics, and more for eight years at The Verge.

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Academic writing often has a reputation for being hard to follow, but what if you could use machine learning to summarize arguments in scientific papers so that even a seven-year-old could understand them? That’s the idea behind tl;dr papers — a project that leverages recent advances in AI language processing to simplify science.

Work on the site began two years ago by university friends Yash Dani and Cindy Wu as a way to “learn more about software development,” Dani tells The Verge, but the service went viral on Twitter over the weekend when academics started sharing AI summaries of their research . The AI-generated results are sometimes inaccurate or simplified to the point of idiocy. But just as often, they are satisfyingly and surprisingly concise, cutting through academic jargon to deliver what could be mistaken for child-like wisdom.

Take this summary of a paper by Professor Michelle Ryan, director of the Global Institute for Women’s Leadership at the Australian National University. Ryan has written on the concept of the “ glass cliff ,” a form of gender discrimination in which women are placed in leadership roles at times when institutions are at their greatest risk of failure. The AI summary of her work? “The glass cliff is a place where a lot of women get put. It’s a bad place to be.”

“It is just excellent,” as Ryan put it.

Ryan tells The Verge the summary was “accurate and pithy,” though it did elide a lot of nuances around the concept. In part, this is because of a crucial caveat: tl;dr papers only analyzes the abstract of a scientific paper, which is itself a condensed version of a researcher’s argument. (Being able to condense an entire paper would be a much greater challenge, though it’s something machine learning researchers are already working on.)

Ryan says that although tl;dr papers is undoubtedly a very fun tool, it also offers “a good illustration of what good science communication should look like.” “I think many of us could write in a way that is more reader-friendly,” she says. “And the target audience of a second-grader is a good place to start.”

Zane Griffin Talley Cooper, a PhD candidate at the Annenberg School for Communication at the University of Pennsylvania, described the AI summaries as “refreshingly transparent.” He used the site to condense a paper he’d written on “ data peripheries ,” which traces the physical history of materials essential to big data infrastructure. Or, as tl;dr papers put it:

“Big data is stored on hard disk drives. These hard disk drives are made of very small magnets. The magnets are mined out of the ground.“

Cooper says although the tool is a “joke on the surface,” systems like this could have serious applications in teaching and study. AI summarizers could be used by students as a way into complex papers, or they could be incorporated into online journals, automatically producing simplified abstracts for public consumption. “Of course,” says Cooper, this should be only done “if framed properly and with discussion of limitations and what it means (both practically and ethically) to use machine learning as a writing tool.”

AI language tools have been incorporated into software from Microsoft and Google

These limitations are still being explored by the companies that make these AI systems, even as the software is incorporated into ever-more mainstream tools. tl;dr papers itself was run on GPT-3, which is one of the best-known AI writing tools and is made by OpenAI, a combined research lab and commercial startup that works closely with Microsoft.

Microsoft has used GPT-3 and its ilk to build tools like autocomplete software for coders and recently began offering businesses access to the system as part of its cloud suite. The company says GPT-3 can be used to analyze the sentiment of text, generate ideas for businesses, and — yes — condense documents like the transcripts of meetings or email exchanges. And already, tools similar to GPT-3 are being used in popular services like Google’s Gmail and Docs, which offer AI-powered autocomplete features to users.

But the deployment of these AI-language systems is controversial. Time and time again, it’s been shown that these tools encode and amplify harmful language based on their training data (which is usually just vast volumes of text scraped off the internet). They repeat racist and sexist stereotypes and slurs and may be biased in more subtle ways, too.

A different set of worries stems from the inaccuracy of these systems. These tools only manipulate language on a statistical level: they have no human-equivalent understanding of what they’re “reading,” and this can lead to some very basic mistakes. In one notorious example that surfaced last year, Google search — which uses AI to summarize search topics — provided misleading medical advice to a query asking what to do if someone suffers a seizure. While last December, Amazon’s Alexa responded to a child asking for a fun challenge to do by telling them to touch a penny to the exposed prongs of a plug socket .

The specific danger to life posed by these scenarios is unusual, but they offer vivid illustrations of the structural weaknesses of these models. Jathan Sadowski, a senior research fellow in the Emerging Technologies Research Lab at Monash University, was another academic entertained by tl;dr papers’ summary of his research. He says AI systems like this should be handled with care, but they can serve a purpose in the right context.

“Maybe one day [this technology will] be so sophisticated that it can be this automated research assistant who is going and providing you a perfect, accurate, high quality annotated bibliography of academic literature while you sleep. But we are extremely far from that point right now,” Sadowski told The Verge . “The real, immediate usefulness from the tool is — first and foremost — as a novelty and joke. But more practically, I could see it as a creativity catalyst. Something that provides you this alien perspective on your work.”

“I could see it as a creativity catalyst. Something that provides you this alien perspective on your work.”

Sadowski says the summaries provided by tl;dr papers often have a sort of “accidental wisdom” to them — a byproduct, perhaps, of machine learning’s inability to fully understand language. In other scenarios, artists have used these AI tools to write books and music , and Sadowski says a machine’s perspective could be useful for academics who’ve burrowed too deep in their subject. “It can give you artificial distance from a thing you’ve spent a lot of time really close to, that way you can maybe see it in a different light,” he says.

In this way, AI systems like tl;dr papers might even find a place similar to tools designed to promote creativity. Take, for example, “Oblique Strategies,” a deck of cards created by Brian Eno and Peter Schmidt. It offers pithy advice to struggling artists like “ask your body” or “try faking it!” Are these words of wisdom imbued with deep intelligence? Maybe, maybe not. But their primary role is to provoke the reader into new patterns of thinking. AI could offer similar services, and indeed, some companies already sell AI creative writing assistants.

Unfortunately, although tl;dr papers has had a rapturous reception among the academic world, its time in the spotlight looks limited. After going viral this weekend, the website has been labeled “under maintenance,” and the site’s creators say they have no plans to maintain it in the future. (They also mention that other tools have been built that perform the same task .)

Dani told The Verge that tl;dr papers “was designed to be an experiment to see if we can make learning about science a little easier, more fun, and engaging.” He says: “I appreciate all of the attention the app has received and thank all of the people who have tried it out [but] given this was always intended to be an educational project, I plan to sunset tl;dr papers in the coming days to focus on exploring new things.”

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AI Research Paper Summary

Welcome to the world of Mindgrasp AI, where we make research paper summaries swift and simple. In a fast-paced academic environment, it’s vital to understand complex research quickly. That’s where AI steps in. Using AI to summarize research papers isn’t just a time-saver; it’s a smart way to grasp key concepts and findings without getting bogged down in details.

Supercharge your learning and teaching with AI!

Click here to experience to check out mindgrap's free trial.

Here’s what we’ll cover in this article:

• Summary of a Research Paper: Discover how Mindgrasp AI captures the essence of research papers, providing you with a concise overview of important findings and methodologies.
• Research Paper Summary Template: Learn about the structure that our AI uses to ensure your summaries are clear, coherent, and comprehensive.
• How to Do a Summary of a Research Paper: Get tips on how to use Mindgrasp AI effectively, turning lengthy papers into concise summaries.
• Summary of a Research Paper Example: See Mindgrasp AI in action with a real example, showcasing how our technology transforms complex information into an easy-to-understand summary.
• Conclusion: We’ll wrap up by highlighting the benefits of using Mindgrasp AI for your research needs, whether you’re a student or a voracious reader.

Join us on this journey to make learning and understanding research papers easier and more efficient with Mindgrasp AI.

ai research paper summary

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Summary of a Research Paper

Summarizing a research paper is a crucial skill in academia and professional research. Here’s why and how it’s done:

• Efficiency in Understanding: Quick grasp of key ideas without reading the entire document.
• Clarity in Communication: Essential for discussing research findings with peers or in presentations.
• Enhanced Retention: Summarizing helps in retaining and recalling the main points.
• Time-saving: Ideal for professionals and students with limited time.

Now, let’s delve into each point:

Efficiency in Understanding

• Summarizing a research paper allows you to understand the core concepts and results quickly. 
• Instead of sifting through pages of complex data and analysis, a summary presents the main points in a digestible format, enabling efficient comprehension.

Clarity in Communication

• A summary provides a clear, concise version of the research, making it easier to communicate the findings to others. 
• Whether it’s for an academic discussion, a class presentation, or a professional meeting, a well-crafted summary ensures that your audience grasps the essential elements of the research.

Enhanced Retention

• The process of summarizing helps in better retention of the material. 
• By focusing on the key points and themes of a research paper, you are more likely to remember and understand the content in the long run.

Time-saving

• In a world where time is precious, summarizing research papers can be a significant time-saver, especially for students and professionals who need to juggle multiple responsibilities. 
• A summary allows you to get the gist of the research without investing hours in reading.

summary of a research paper

Mindgrasp AI acts as your personal research paper summarizer, streamlining this process. It uses advanced algorithms to identify and extract key points, ensuring that you receive a comprehensive yet concise summary. This tool is perfect for anyone looking to save time, enhance understanding, and improve retention of complex research papers. With Mindgrasp AI, you can focus on what’s truly important – applying the knowledge gained from research in your academic or professional life.

“Upload everything from powerpoint's to books or videos and generate anything from summaries to notes, flash cards and quizzes.”

Research Paper Summary Template

Finding the right template for summarizing research papers can streamline the process, ensuring that your summaries are both effective and efficient. Here’s where to find them and how they can be useful:

• University Websites and Libraries
• Many universities offer online resources, including templates for research paper summaries. 
• These templates are often tailored to academic standards and can be found through university libraries or academic support centers.
• Utilizing these templates ensures adherence to academic norms, helping students present their summaries in a format that’s recognized and respected in the academic world.
• Professional Journals and Publications
• Leading journals and publications often provide guidelines and templates for summarizing research. 
• These can serve as excellent references, especially for professionals in the field.
• Following these templates helps in aligning your summary with industry standards, which is crucial for professional credibility and clear communication of research findings.
• Online Educational Resources
• Websites dedicated to academic writing and research skills frequently offer a variety of templates and guides. 
• These resources cater to a wide range of disciplines and research topics.
• Accessing these online templates can provide flexibility and variety, allowing you to choose a format that best suits your specific research topic and audience.

Using a research paper summary template from these sources ensures that your summaries are structured, coherent, and focused on the key elements of the research. It guides you in highlighting the main points, methodologies, and conclusions effectively. Mindgrasp AI complements these templates by providing a structured and automated approach to summarizing, making the process even more efficient and tailored to your specific needs. With Mindgrasp AI, you get the best of both worlds – the precision of AI technology and the clarity of a well-designed template.

research paper summary template

How to Do a Summary of a Research Paper

Creating a summary of a research paper involves distilling the most critical information into a concise and readable format. Here’s how to do it and what to include for an effective summary:

• Identify the Main Objectives and Findings
• Begin by pinpointing the primary goals and conclusions of the research. 
• This helps in understanding the purpose and the outcomes of the study.
• Clearly stating the research objectives and findings in your summary provides a straightforward overview of what the study accomplished and its significance.
• Outline the Methodology
• Summarize the methods used in the research to give context to the findings. 
• This includes the research design, data collection techniques, and analysis methods.
• Including the methodology offers insight into how the research was conducted and supports the credibility of the findings.
• Highlight Key Points from the Discussion and Conclusion
• Extract the most important points from the discussion and conclusion sections. 
• These often include interpretations of the results, implications, and recommendations.
• Focusing on these aspects ensures that your summary captures the essence of the research’s impact and potential future directions.
• Maintain Clarity and Brevity
• Aim for clarity and brevity in your writing.
• Avoid technical jargon unless necessary and keep the summary concise while covering all vital aspects of the paper.
• A clear and brief summary makes the information accessible to a broader audience, enhancing the reach and utility of the research.

By following these steps, you can create a comprehensive and effective summary of a research paper. Mindgrasp AI can further assist in this process by automatically extracting key points and presenting them in an organized format, ensuring that your summary is both accurate and efficient. With Mindgrasp AI, you can confidently tackle research summaries, saving time and enhancing your understanding.

 how to do a summary of a research paper

Summary of a Research Paper Example

Finding examples of research paper summaries is an excellent way to ensure you’re on the right track. Here are some reliable sources for such examples:

• Academic Journals and Online Databases
• Many academic journals provide abstracts or summaries of the research papers they publish. 
• These can be accessed through online databases like JSTOR, PubMed, or Google Scholar.
• Reviewing these summaries offers insights into how experts in various fields condense complex research into concise, informative overviews.
• University Writing Centers and Libraries
• University writing centers often have resources, including examples of research paper summaries. 
• Libraries may also have guides or sample summaries available.
• Utilizing these resources ensures that you’re following academic conventions and standards in your summaries.
• Educational Websites and Online Courses
• Websites dedicated to academic writing and research skills often provide examples of research paper summaries. 
• Online courses on research methodologies and academic writing can also be helpful.
• These resources can offer a variety of examples across different disciplines, giving you a broader perspective on summary writing.
• Professional Research Summarization Services
• Services like Mindgrasp AI often showcase examples of their summarization work, including research papers.
• Examining these examples can give you an idea of how AI-powered tools approach the task of summarizing complex research, offering a blend of accuracy and comprehensibility.

Incorporating these resources into your research process can enhance your ability to create effective summaries. By analyzing examples from these sources, you can learn how to capture the essence of a research paper succinctly and accurately, a skill that Mindgrasp AI can further facilitate with its advanced summarization capabilities.

summary of a research paper example

Start Using Mindgrasp AI to Summarize Your Research Papers

To wrap up, let’s revisit the key points we’ve discussed:

• Efficiency and understanding are enhanced when you summarize research papers .
• Templates, like those found in academic journals and online resources, provide a structured approach to summaries.
• Knowing how to summarize involves identifying main objectives , outlining methodology, and highlighting key discussion points.
• Examples of summaries, accessible through academic databases and university resources, guide you in crafting effective summaries.
• Mindgrasp AI aligns with academic and professional standards for concise and clear summaries.
• The AI tool is an invaluable asset for both students and professionals , saving time and enhancing comprehension.

Summarizing research papers is more than a task; it’s an essential skill that enhances understanding and communication in academic and professional settings. It saves time, ensures clarity, and aids in retaining critical information. Enter Mindgrasp AI – your efficient and effective solution for summarizing research papers. With its advanced AI capabilities, Mindgrasp AI streamlines the summarization process, ensuring accuracy and coherence. Embrace this tool to transform the way you interact with complex research, making learning and professional development more accessible and efficient than ever before.

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Feature Featire

COMPETITORS

Academic Orientation

Designed with academic rigor in mind, ensuring your summaries uphold scholarly standards.

Often lack academic focus, potentially diluting the essence of scholarly texts.

Contextual Understanding

Employs advanced AI to grasp the context, ensuring summaries are meaningful and coherent.

May struggle with contextual understanding, leading to disjointed or misleading summaries.

Customization

Offers customization options to tailor summaries according to your specific needs and preferences.

Generic summarization often with limited customization, risking loss of critical information.

User-Friendly Interface

Intuitive interface makes summarization a breeze, enhancing the user experience.

Clunky interfaces can hinder the summarization process, making it less user-friendly.

Promotes an interactive learning environment, aiding in improving your summarization skills over time.

Merely provide summarization with no added value in terms of learning or skill enhancement.

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Learning and Verification of Task Structure in Instructional Videos

Link to paper The full paper is available here. You can also find the paper on PapersWithCode here. Abstract Introduce a new pre-trained video model, VideoTaskformer, focused on representing the semantics and structure of instructional videos Pre-train VideoTaskformer by predicting weakly supervised textual labels for steps that are randomly masked out from an instructional video Learn step representations globally, leveraging video of the entire surrounding task as context Introduce two new benchmarks for detecting mistakes in instructional videos Introduce a long-term forecasting benchmark Outperforms previous baselines on these tasks Evaluate VideoTaskformer on 3 existing benchmarks and achieves new state-of-the-art performance Paper Content Introduction Trying to build a bookshelf using a YouTube video Need to repeatedly hit pause on the video Interactive assistant can guide user through task Composite task involves multiple fine-grained activities Ideal assistant has high-level and low-level understanding Prior work models step representations from single short video clips VideoTaskformer learns step representations for masked video steps Mistake detection task and dataset for verifying video representations VideoTaskformer learns step representations for whole video Network learns to predict labels for masked steps Representations improve performance on downstream tasks VideoTaskformer capable of detecting mistake types Related works Large-scale narrated instructional video datasets enable learning joint video-language representations and task structure from videos Assembly-101 dataset and Ikea ASM provide videos of people assembling and disassembling toys and furniture Existing benchmarks for evaluating representations learned on instructional video datasets include step localization, step classification, procedural activity recognition, and step forecasting Recent works attempt to learn procedures from instructional videos Video action recognition models have improved over the last few years Works learn representations for longer video clips containing semantically more complex actions Learning task structure through masked modeling of steps Goal is to learn task-aware step representations from instructional videos Developed VideoTaskformer, a video model pre-trained using a BERT style masked modeling loss Masking is done at the step level Framework consists of two steps: pre-training and fine-tuning Pre-training is done on weakly labeled data During fine-tuning, a subset of the parameters is adjusted using labeled data from the downstream tasks Pre-training approach uses masked step modeling loss Step modeling extends masked language modeling techniques used in BERT and VideoBERT Evaluated on 6 downstream tasks Step representations are learned from entire video with all steps as input Step classification and distribution matching are used as training objectives Downstream tasks Mistake step detection: Identify which step in a video is incorrect Mistake ordering detection: Verify if the steps in a video are in the correct temporal order Short-term forecasting: Predict the step label given the previous n segments Long-term step forecasting: Predict the step labels for the next 5 steps given a single step Procedural activity recognition: Recognize the procedural activity (i....

DreamBooth3D: Subject-Driven Text-to-3D Generation

Link to paper The full paper is available here. You can also find the paper on PapersWithCode here. Abstract Presents DreamBooth3D, an approach to personalize 3D models from 3-6 images Combines text-to-image and text-to-3D models Naively combining these methods fails to yield satisfactory 3D assets Uses 3-stage optimization strategy to leverage 3D consistency and personalization Produces high-quality, subject-specific 3D assets with text-driven modifications Paper Content Introduction Text-to-Image (T2I) generative models can create and edit visual content Recent works have demonstrated high-quality Text-to-3D generation Applications in graphics, VR, movies, and gaming Text prompts allow for some degree of control, but difficult to precisely control identity, geometry, and appearance Recent success in personalizing T2I models for subject-specific 2D image generation DreamBooth3D proposed for subject-driven Text-to-3D generation Given a few casual image captures of a subject, generate subject-specific 3D assets Draws inspiration from recent works 3-stage optimization framework proposed Synergistic optimization of NeRF and T2I models Results indicate realistic 3D assets with high likeness to given subject Related works Text-to-Image Generation uses GANs, autoregressive models, and masked image models to generate images Denoising diffusion models can generate high-quality images and be conditioned on various inputs 3D Generation uses 3D reconstruction from images or generative models from image collections Text-to-3D methods generate 3D assets from text prompts Subject-driven Generation enables users to personalize image generation for specific subjects Textual Inversion optimizes for a new “word” in the embedding space of a pre-trained text-to-image model Approach Input consists of k casual subject captures with n pixels and a text prompt Aim is to generate a 3D asset that captures the identity and is faithful to the text prompt 3D assets are optimized in the form of Neural Radiance Fields Problem is more challenging than typical 3D reconstruction setting Technique is based on advances in Text-to-3D optimization and personalization Preliminaries DreamBooth is a method to personalize a text-to-image (T2I) diffusion model....

The Quantization Model of Neural Scaling

Link to paper The full paper is available here. You can also find the paper on PapersWithCode here. Abstract Proposed Quantization Model explains power law dropoff of loss with model and data size Quantization Hypothesis states that learned network capabilities are quantized into discrete chunks Power law in use frequencies explains observed power law scaling of loss Validated prediction on toy datasets and studied scaling curves for large language models Paper Content Introduction Larger neural networks trained on more data perform better than smaller neural networks trained on less data Mean test loss decreases as a power law in both the number of network parameters and the number of training samples Larger models often have emergent abilities, i....

Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense

Link to paper The full paper is available here. You can also find the paper on PapersWithCode here. Abstract Detection algorithms have been proposed to identify AI-generated text A 11B parameter paraphrase generation model (DIPPER) was trained to paraphrase paragraphs Several detectors were tested and found to be evaded by DIPPER A defense was introduced to increase robustness of AI-generated text detection to paraphrase attacks Code, model and data will be open sourced for future research Paper Content Introduction LLMs can write coherent and relevant longform text Fears of malicious applications such as fake news and homework answers Algorithms proposed to detect machine-generated text Unclear how robust these algorithms are to paraphrase attacks Demonstrate vulnerability of existing detectors to paraphrase attacks Train 11B parameter paraphrase generation model called DIPPER DIPPER can paraphrase paragraph-length texts DIPPER has two features to help evade AI-generated text detectors Attack several recently proposed AI-generated text detection algorithms Experiments show all detection algorithms misclassify AI-generated texts Propose to use retrieval methods to detect AI-generated text 97....

3D-POP -- An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture

Link to paper The full paper is available here. You can also find the paper on PapersWithCode here. Abstract Recent advances in machine learning and computer vision are revolutionizing the field of animal behavior. Large datasets of annotated images of animals for markerless pose tracking are still scarce. A method is proposed that uses a motion capture system to obtain a large amount of annotated data on animal movement and posture....

Reinforcement Learning with Exogenous States and Rewards

Link to paper The full paper is available here. You can also find the paper on PapersWithCode here. Abstract Exogenous state variables and rewards can slow reinforcement learning. Reward function decomposes additively into endogenous and exogenous components. Decomposition of state space into exogenous and endogenous state spaces must be discovered. Algorithms introduced to discover exogenous and endogenous subspaces of state space. Experiments show that these methods produce speedups in reinforcement learning....

Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions

Link to paper The full paper is available here. You can also find the paper on PapersWithCode here. Abstract Proposed method for editing NeRF scenes with text-instructions Uses an image-conditioned diffusion model (InstructPix2Pix) Iteratively edits input images while optimizing underlying scene Results in optimized 3D scene that respects edit instruction Able to edit large-scale, real-world scenes More realistic, targeted edits than prior work Paper Content Introduction Capturing a realistic digital representation of a real-world 3D scene is easy Captured 3D content is replacing traditional processes of manually-generated assets Tools for editing 3D assets are underdeveloped Text instructions can be used to edit 3D scenes 2D diffusion model is used to extract shape and appearance priors Related work NeRFs are a popular approach for generating photorealistic novel views of a scene Editing NeRFs is a challenge Physics-based inductive biases can be used to enable changes in materials or scene lighting Bounding boxes can be used to allow easy compositing of different objects and spatial manipulations Cli-mateNeRF extracts rough geometry from a NeRF and uses physical simulation to apply weather changes Most physically-based edits revolve around changing physical properties of the reconstructed scene Recent works have explored artistic 3D stylization of NeRFs EditNeRF explores editing NeRFs by manipulating latent codes learned from object categories ClipNeRF and NeRF-Art extend this line of work by encouraging similarity between CLIP embeddings of the scene and a short text prompt Recent progress in pre-trained large-scale models has enabled rapid progress in the domain of generating 3D content from scratch Instruction-based 2D image-conditioned diffusion model enables purely language-based interface for 3D editing Method Takes as input a reconstructed NeRF scene, source data, and a natural-language editing instruction Outputs an edited version of the NeRF and input images using a diffusion model and NeRF training Background Neural radiance fields (NeRFs) are a way to represent and render a 3D scene....

FeatureNeRF: Learning Generalizable NeRFs by Distilling Foundation Models

Link to paper The full paper is available here. You can also find the paper on PapersWithCode here. Abstract Recent works have shown promising results on novel view synthesis from single or few images. Models have rarely been applied on other downstream tasks beyond synthesis such as semantic understanding and parsing. Proposed framework named FeatureNeRF to learn generalizable NeRFs by distilling pre-trained vision foundation models. FeatureNeRF maps 2D images to continuous 3D semantic feature volumes, which can be used for various downstream tasks....

LFM-3D: Learnable Feature Matching Across Wide Baselines Using 3D Signals

Link to paper The full paper is available here. You can also find the paper on PapersWithCode here. Abstract Finding correspondences between images of the same object is important for understanding its geometry. Recent years have seen progress in this area due to deep learning based local image features and learnable matchers. Learnable matchers often underperform when there is only small regions of co-visibility between image pairs. We propose a Learnable Feature Matching framework that uses models based on graph neural networks and integrates 3D signals to boost correspondence estimation....

Can we trust the evaluation on ChatGPT?

Link to paper The full paper is available here. You can also find the paper on PapersWithCode here. Abstract ChatGPT is a large language model with mass adoption Evaluating ChatGPT’s performance is challenging due to its closed nature and continuous updates Data contamination is an issue when evaluating ChatGPT Stance detection is used as a case study to highlight the issue of data contamination Fair model evaluation is a challenge in the age of closed and continuously trained models Paper Content Introduction Methods Zhang et al....