In the fast-paced world of scientific research, staying updated with the latest papers can be a daunting task. With the sheer volume of papers published daily, researchers often find themselves overwhelmed by the volume of scientific information to digest.
Enter AI Reading.
AI Reading means using artificial intelligence to streamline the process of reading and understanding scientific papers, making it faster, more efficient, and more fun for researchers.
Summarization
DIY One of the simplest ways to leverage AI for reading scientific papers is through DIY summarization. By pasting the paper or its abstract into a language model like ChatGPT, researchers can quickly obtain a concise summary: " summarize this paper." This method allows users to grasp the core ideas of a paper without having to read it in its entirety. With upcoming new chat interfaces, we can even attach the PDF to the Chat and ask for “write a summary”.
Automated TLDRs For those looking for a more automated solution, platforms like Semantic Scholar offer an innovative feature called TLDR (Too Long; Didn’t Read). This tool automatically generates a brief summary of research papers, highlighting the essential points. The TLDR feature is also available through an API, making it accessible for developers who want to integrate summarization capabilities into their own applications. The code is on Github.
Interactive Chat Interfaces for Interactive Reading
Concept. Now; one can do interactive reading. The idea is that researchers pose targeted queries to an AI system about the paper’s content. This method transforms passive reading into an active dialogue, allowing users to probe deeper into complex topics, clarify ambiguities, and extract specific insights without reading the entire document. For instance, one might ask, “What are the key findings of this study?” or “How does this method compare to previous work?” This technique leverages AI’s natural language processing to provide contextual answers, making it easier to understand nuanced details and relate them to broader research contexts.
This capability not only aids in comprehension but also encourages deeper engagement with the material, as users can clarify doubts and explore concepts in real-time.
This is now supported by all chat interfaces (ChatGPT, Claude, etc) via upload PDF or copy pasting paper URLs. Google NotebookLM allow users to talk about multiple papers at the same time and save the dialogue in a nice way.
Adobe Acrobat Reader also has a feature, called AI Assistant and Generative Summaries, I’ve never tried it.
AI Overlays for Enhanced Reading
Several platforms have introduced AI overlays that enhance the reading experience of scientific papers:
Semantic Scholar Reader: This tool includes an AI Skimming feature that highlights key components of a paper: goals, methods, and results. By emphasizing these critical sections, researchers can quickly identify the paper’s contributions and relevance to their work.
Google Scholar PDF Reader: Another noteworthy tool is the AI Outline feature in Google Scholar’s PDF reader. This feature generates an outline of the paper, allowing users to navigate through the content more efficiently. By providing a structured overview, researchers can easily locate specific sections of interest without having to scroll through the entire document.
AI Paper Search & Automated reviews
The systematic literature review is a critical component of academic research, traditionally requiring extensive manual effort to synthesize existing knowledge and identify research gaps. Now we have AI-powered tools for this like OpenAI Deep Research, Perplexity Deep Research or SciSpace Deep Review, Ai2 Scholar QA, Gemini Deep Research
They employ a multi-agent architecture that simulates human researcher behavior, including query optimization, multi-query execution, citation graph traversal, structured knowledge extraction, and self-termination heuristics.
In comparative analyses, SciSpace Deep Review outperforms competing tools such as OpenAI’s Deep Research and Perplexity in terms of speed, precision, and citation coverage.
Related work
This is an active field with lots of startups:
- undermind is AI search.
- scite is AI search and AI reference check.
- Google Scholar Labs supports asking questions to find evidence.
- Elicit, Consensus, ResearchRabbit.
Conclusion
The integration of AI into the reading process of scientific papers is revolutionizing how researchers access and understand information. From summarization to AI-enhanced reading tools, these innovations are designed to save time, improve comprehension and enable better science.
By embracing these AI-driven reading solutions, researchers can enhance their productivity and stay at the forefront of their fields, making the daunting task of reading scientific literature more manageable and efficient on a daily basis.
Martin Monperrus
November 2024
Disclosure: AI has helped to write this post