Paper2md – convert papers to Markdown to be used for LLM context (github.com)

A new tool, Paper2md, has been launched to streamline the conversion of academic PDFs into structured Markdown summaries tailored for use with large language models (LLMs). This innovation automates the extraction of titles and essential content from PDF documents using a hierarchical approach that considers metadata, text heuristics, and filename fallbacks. The summarization process employs an OpenAI-compatible API with a map-reduce strategy, allowing for high-quality outputs that encompass a wide variety of LLM providers, including OpenRouter and Gemini. This development is significant for the AI/ML community as it facilitates easier access to academic research, enabling engineers and developers to quickly integrate relevant findings into their codebases. The structured summaries produced include sections like TL;DR, problem statements, methodologies, results, and practical takeaways. Furthermore, the tool is designed with domain-specific features, offering insights related to deals and product recommendations, which can enhance application development. By streamlining the extraction and summarization process, Paper2md stands to improve the efficiency of researchers and practitioners in leveraging complex academic research for practical applications.