AI Tools for Summarizing AI Research

March, 11th, 2024.

Hey Everyone,

I often get questions about the best AI tools to summarize PDFs and to explore AI research papers. I reached out to of to help us explore the systems he uses and some methods for staying updated on AI using AI tools.

As such, this article is a guide and a more actionable piece for readers dedicating some time to learn more about the space.

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Logan’s Newsletter is around 7 months old, and uncovers the engineering behind the AI you use every day. He’s currently a Software Engineer at Google.

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In this article Logan will also break down the best tools to make AI research easier and summarize PDFs. This is such a useful piece you may decide to share it.

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By , February, 2024. ~ 📖 For a better reader experience, read on the web.

The boom of AI has seen an incredible uptick in new research papers, making it almost impossible to stay on top of AI advancements. The numerous applications of AI and the speed of iteration within AI research have made (and continuously make) AI advance at a fast rate. Online platforms like arxiv.org make it really easy for research teams to get their research out to the world as quickly as possible. To put it into perspective, as I’m writing this, there have been almost 1500 new machine learning-related research papers submitted to arxiv.org over just the past week. 

The speed of AI research being put online makes it not only difficult to read every paper, but it also makes it tough to identify the papers worth reading. All areas of research suffer from the push to release new things as quickly as possible, causing papers to be written without substantial evidence for their claims, without any novel developments, and sometimes even with purposefully fraudulent conclusions. Researchers may be able to pick these things out quickly, but for anyone else, this is a difficult task. Luckily, AI itself makes the process of staying up-to-date on AI research much easier for anyone. AI has not only made research faster but also more accessible. Some noteworthy use cases of AI on the field of research in general are:

Fraud detection: AI has been used to identify fraudulent research even from some of the most distinguished scientists in their respective fields. Fraudulent research is a huge cost to the general public as it tends to drive money toward incorrect conclusions and can even be potentially life-threatening when used for things such as medical research.

Plagiarism detection: AI algorithms are very good at recognizing patterns within data, which makes them an effective tool at identifying similar information between documents. This can be used to find plagiaristic research to ensure credit is given where due and correct information is cited.

Summarization: large language models (LLMs) have given rise to cohesive summarization of a body of text. This also includes technical documents where they are effective at concisely highlighting important information.

For the purposes of this article, I’m going to focus on the methods and tools that allow AI to summarize technical documents. AI has made it really easy for anyone (even without technical knowledge of a field) to understand the key conclusions of research papers. After reading this article you should have a good understanding of:

Why AI summarization is so useful specifically for research papers

The types of AI summarization and why you should understand them

Where to source AI research papers

The best tools for AI research paper summarization

Why AI Summarization is Useful Specifically for Research Papers

Abstracts Aren’t Comprehensive Enough

Reading just the abstract of a research paper doesn’t provide a thorough understanding of the entire study. Abstracts are useful to understand if a paper addresses the information you’re looking for, but lack an adequate summarization of findings because they are limited in length, simplify nuance, selectively present material, and lack supporting evidence for drawing conclusions.

AI’s Role in Answering Specific Questions

By utilizing natural language processing algorithms, AI can extract key information, identify inconsistencies, and provide insights that aid in evaluating the validity of conclusions. This capability is crucial as it allows researchers to pose targeted questions about the data, methodology, or results presented in the document, enabling them to assess the robustness of the conclusions drawn. AI’s role in answering questions not only enhances the understanding of complex research but also serves as a valuable tool for fact-checking and validating the accuracy of conclusions, ultimately contributing to the credibility and reliability of the research findings.

Let’s Understand AI Summarization

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An Overview of Text Summarization Methods from Simple to Complex

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One of the most useful capabilities of modern AI (particularly LLMs) is their ability to summarize information. This is especially true for scientific topics as LLMs can condense complex information into simplified, understandable summaries. This is super useful for reading large amounts of research papers. Within AI, there are two primary types of summarization: extractive summarization and abstractive summarization. 

Extractive summarization involves selecting and combining important sentences or phrases from the original text to create a summary. This approach does not generate new sentences but rather extracts and reorganizes existing content.

Benefits:

Retains the original wording and style of the text.

Generally produces grammatically correct summaries.

Easier to implement and requires less computational resources compared to abstractive summarization.

Disadvantages:

May result in incoherent or redundant summaries.

Relies heavily on the quality of the sentence or phrase extraction algorithms.

Abstractive summarization, on the other hand, involves interpreting and paraphrasing the original text to generate a summary that may contain new words, phrases, or sentences not present in the source material. This approach aims to produce more human-like summaries.

Benefits:

Has the potential to generate more informative and coherent summaries.

Can capture the underlying meaning of the text and express it in a more concise form.

Allows for the creation of novel content, making it suitable for tasks such as headline generation.

Disadvantages:

More challenging to implement due to the need for natural language generation and understanding techniques.

Prone to generating grammatically incorrect or factually inaccurate summaries.

Requires larger training datasets and more computational resources compared to extractive summarization.

Whether over a single document or across multiple documents, extractive vs. abstractive summarization is a very important distinction to understand. Due to LLMs, most people are used to abstractive summaries; however, abstractive summaries struggle with hallucination. This means you need to hold your summarization tools to a higher standard to make sure they aren’t fabricating information.

I personally am partial toward AI summarization methods that use a mixture of extractive and abstractive methods to provide a cohesive summary, while also ensuring the summarizer pulls information directly from the source material.

If you’d like to read more about extractive vs. abstractive summarization, you can read this article about how the implications behind extractive vs. abstractive summarization affects the healthcare industry that I pulled the image for this section from.

Where to Find Research Papers

These are the sources for AI research papers that I personally use to stay updated on AI advancements:

1. arXiv: arXiv is a popular repository for preprints in various scientific disciplines, including artificial intelligence. It is a go-to platform for researchers to share their latest work before formal publication. This is the most common place for excellent AI research to exist, but that research can be a pain to find.

2. ACM Digital Library: The Association for Computing Machinery (ACM) Digital Library is a comprehensive database of full-text articles and conference proceedings in the field of computing, and it offers a wealth of AI research papers. I’ve found it easier to find the information I’m looking for in the ACM library, but the newest AI advancements don’t get there as quickly as they get to arXiv.

3. Google Scholar: Google Scholar is a freely accessible web search engine that indexes scholarly articles across various disciplines, making it a valuable resource for finding AI research papers from different sources. Similar to arXiv, Scholar has a lot of excellent sources, but might take some searching to find what you’re looking for.

4. Perplexity: Perplexity is an LLM-based search engine that finds information on the internet, summarizes it, and links to resources. I’ve been using its dedicated “Academic” focus to help me find AI research papers that contain important advancements in the field of AI. It has been an incredible tool for finding papers, but it doesn’t give the same level of selection as the above sources.

5. X: I know a lot of people aren’t particularly fond of X in its current state, but it continues to be the leading social platform for sharing tech content. This is especially true for highly technical content such as research findings. Researchers and their teams will often share their findings and field questions on them by posting them on X. Unfortunately, this requires an X account and it also requires a good bit of work to find people worth following. If you’re specifically looking for a list of machine learning experts to follow, I’ve curated one here.

Your source of information is important for finding high-quality information and also to ensure you’re keeping up-to-date on the latest in AI research.

AI Tools to Understand AI Research

Let’s get into the tools you can use to make AI research easier. For each tool, I’ll say what it does, a brief overview of how it works, and what I think it’s most useful for.

PDF GPT

PDF GPT is a tool that allows you to upload PDF documents to get answers, summaries, translations, and citations. It utilizes OpenAI’s GPT on the backend to let you chat with the document and ask questions. PDF GPT is specifically tuned for summarization and determining high-importance topics within a document. It’s particularly useful for translating content into multiple languages and can even generate study questions related to the topics within the PDF.

It’s free for uploading a single document. If you want to chat about multiple PDFs, the pro subscriptions costs $5/mo.

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Adobe AI Assistant

I haven’t tried this one yet, but Adobe announced an AI assistant that can search documents for information and summarize documents for you. It also provides a similar chat-like interface for you to ask questions about the document. It’s designed to be particularly good at extracting information from long documents and summarizing the concepts presented within them. This PDF assistant will likely be built on top of Adobe Sensei, Adobe’s generative AI model. The AI assistant will also be able to generate citations and text for emails, presentations, and reports from the contents of the document.

This is a particularly useful product because it’s built directly into Adobe Reader and Adobe Acrobat, which many consumers already use for interacting with PDFs. This tool is currently in beta and will be a subscription-based model after release.

Learn More

Perplexity

Perplexity is an LLM-based search engine that can use multiple different models under the hood to search the internet, find sources, and extract information from them. Perplexity uses both extractive AI methods to pull information from sources and abstractive methods to provide a coherent summary of a user’s search query. You can also use Perplexity to summarize PDFs by attaching the PDF to your question. This allows you to receive a concise summary and also ask follow-up questions. There is also a Chrome extension that makes it super easy to summarize any webpage, although the extension struggles to summarize PDFs viewed in-browser.

Perplexity has been my favorite all-in-one LLM tool to use recently. It has the ability to shift ‘Focus’ based on how you want it to assist you. You can have a general LLM search experience or shift toward academic results, videos, or even Reddit discussions. It also integrates with WolframAlpha and can write for you.

The features I use within Perplexity are entirely free. There’s a paid tier to allow users greater customization of their search experience as well as access to a greater number of sources per query. Perplexity is available online and also has an app for Android and iOS.

Try Perplexity Pro

Gemini 1.5

Gemini is Google’s LLM similar to ChatGPT. It can be accessed via the chat interface at gemini.google.com, the Gemini app (on Android)/Google app (on iOS), API, or Google’s AI interfaces such as AI Studio and Vertex AI. While I think purpose-built LLMs are better for understanding AI research papers, I include Gemini on this list because Gemini 1.5 is the first LLM to have a 1 million-token context window. This means the LLM can be given very large documents (even full textbooks) and accurately summarize and pull concepts from them. This is extremely useful for summarizing many related documents and other long-form text.

Currently, Gemini 1.5 is only available in beta on AI Studio and Vertex AI. I’m unsure what the cost will be in the future when it is more widely available. I’ve included it here because it’s likely that sometime in the near future, larger context windows will be making it to the LLMs most consumers are already using. This will make the synthesis and summarization of information for generic LLMs even more effective.

Go to Google AI Studio

ChatGPT

I’m finishing this section off with the most well-known LLM AI assistant: OpenAI’s ChatGPT. For those unacquainted, ChatGPT is OpenAI’s chatbot built on top of their popular GPT LLMs. It was the first LLM chatbot to hit the tech world back at the end of 2022 and revolutionized the way AI assistants are used. My experience with ChatGPT has been positive. It’s a generic AI assistant that can do pretty much anything an LLM is capable of with good quality. I’ve found that it struggles to match up to the competition for more specific use cases, but it is more than capable of answering a query of “Can you please summarize this for me?” ChatGPT is also integrated nicely with the products you use each day. It has a high-quality smartphone app capable of doing anything the web interface can, as well as a relatively easy-to-use API for any developers.

ChatGPT is useful as an all-around generic LLM assistant. If you’re looking to summarize AI research and don’t want to use another tool and you’re already using ChatGPT, it may be the way to go. This is especially the case if you’re already paying for ChatGPT Plus. While ChatGPT is free, Plus gives you higher-quality summarizations, allows for ‘custom GPTs’ (meaning you can narrow down on ChatGPT’s generic use case), and has the ability to generate images within the chatbot using DALLE-3.

I include ChatGPT last because it isn’t my option of choice, but also because it’s important to understand the privacy implications behind using it. Any questions asked or data sent to ChatGPT will be used by OpenAI for training GPT models. This may not seem like a huge deal, but there has been research evidence showing just how easy it is to make LLM chatbots spit out their training data and many people have used ChatGPT for personal and business reasons that require inputting sensitive data. OpenAI wasn’t entirely transparent about it until just recently and it took many ChatGPT users by surprise. To clarify, using GPT via OpenAI API does not consent for any data to be used in training–but using ChatGPT does.

Local, Open-source LLMs

All of the above use cloud LLMs to summarize documents. This means your data and requests are routed through a company’s LLM and the outputs are returned to you. This is beneficial to you because the companies take care of the infrastructure and engineering required to make a quality product with an LLM. It also gives you access to a company’s proprietary offering and they’ll set up the proper interface for you to interact with their model. For example, a lot of companies have a chatbot offering that allows you to easily interact with their machine learning model via a chat interface.

Thanks to open-source LLMs, you also have the option of running your own LLM locally. Instead of the LLM being hosted and controlled by a company in the cloud, you can have the LLM running on your computer. This allows you to control and customize the LLM for your use case and ensure your information submitted to the LLM is secure and private. This requires a bit of know-how to set up and interact with the local LLM and a computer with enough RAM to run the LLM locally. If you’re interested in learning how to do this, reach out to me and I can get you started.

Your greatest AI learning resource

Leverage the greatest resource we have for learning about technical topics: the experts within the field. Subscribing to newsletters and following AI experts online for summaries of AI advancements and topics is incredibly valuable for staying informed about AI. These experts curate the most relevant and cutting-edge information, provide concise summaries, detail breakthroughs, and clarify the impacts of research advancements. The best ways to get this information are from:

Signing up for newsletters (just like the one you’re reading!)

Following experts on X: I know this isn’t ideal for many, but X continues to be a great place for technical information.

Following experts on Substack Notes: I’ve been surprised at the rapid growth of Notes in the past few months and it’s been great to see many AI experts sharing information there.

Using these resources will not only help you stay updated on AI, it will also clarify complex topics and allow you to learn about why AI matters from those who know more about it, and even ask them questions in comments and replies.

If you have any questions about AI summarization, finding research papers, AI tools, or AI/ML in general, feel free to reach out to me. 

About the Guest Contributor

I’m Logan Thorneloe and I work as a machine learning engineer at Google. In my spare time I tinker with models, build software tools, and create free machine learning educational resources.

I write Society’s Backend on Substack to help anyone understand the engineering behind the AI they use every day. You can chat with me on Substack, LinkedIn, and X.

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I’m grateful to meet such talented and passionate writers, analysts, researchers and educators. Note that when this was written, Claude 3 Opus had yet to come out and would also likely be a good option for summarizing AI papers and PDFs.

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