ChatPaper leverages large language models (LLMs) to automate the summarization and analysis of academic papers, aiming to accelerate research workflows for scientists and students. It provides a suite of tools for tasks like paper summarization, translation, polishing, and review, significantly reducing the time spent on literature review and comprehension.

How It Works

The core of ChatPaper involves using LLMs, primarily ChatGPT, to process paper content. It extracts key sections (abstract, introduction, methods, conclusion) and feeds them to the LLM for summarization based on a structured query format (background, past solutions, proposed methods, results). For local PDFs, it parses the document content before sending it to the LLM. The project also integrates with arXiv to fetch and process papers based on keywords.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python 3.9+, OpenAI API key, global proxy (e.g., Clash).
• Usage:
  • ArXiv search & summarize: python chat_arxiv.py --query "your query" --filter_keys "your keywords" --max_results N
  • Local PDF summarize: python chat_paper.py --pdf_path "path/to/your.pdf"
• Links: Official Website, Bilibili Tutorial, Documentation

Highlighted Details

• Batch Processing: Can automatically summarize up to 1000 papers from ArXiv based on keywords.
• Multi-functionality: Includes paper summarization, translation, polishing, review, and even generating XMind notes from papers (ChatPaper2Xmind).
• Local PDF Support: Processes local PDF files for summarization and translation.
• Academic Review Assistance: Features like ChatReviewer and ChatResponse aid in paper critique and responding to reviewer comments.

Maintenance & Community

The project has a vibrant community, evidenced by its 7.2k+ stars on GitHub. Recent updates include local PDF translation and XMind note generation. The project encourages community contributions and feedback.

Licensing & Compatibility

The project is open-source, with code available under a permissive license, facilitating commercial use and integration into closed-source projects.

Limitations & Caveats

The summarization quality can vary, and users are advised to double-check numerical details against the original paper. Some features, like Docker deployment, may have path issues. The project does not support review-style articles.