Daily Paper Update: Automated Academic Paper Tracking and Structured Abstract System

This article introduces an open-source automated academic paper tracking project—Daily Paper Update. The project periodically crawls papers from top platforms/conferences like arXiv, NeurIPS, and ICML, generates structured abstracts, and helps researchers efficiently keep up with cutting-edge trends. Key features include automated operation driven by GitHub Actions, multi-dimensional intelligent analysis, structured and readable output, and an open-source collaboration mechanism.

In the field of AI and machine learning, academic papers are produced at an extremely fast pace: arXiv adds hundreds of new papers daily, and top conferences like NeurIPS and ICML accept over 10,000 papers annually. Traditional manual retrieval methods are time-consuming and labor-intensive, and it's easy to miss important work. Daily Paper Update aims to solve this problem by providing automated tracking and structured abstract services.

The system is driven by GitHub Actions and runs every 2 hours. Core components include:

1. Data Collection Layer: Crawls paper information from platforms/conferences like arXiv, NeurIPS, ICML, ICLR, ACL, and CVPR via scripts;
2. Intelligent Analysis Layer: Performs keyword/topic detection, methodology analysis, complexity rating (high/medium/low), and statistical indicator calculation;
3. Structured Output Layer: Organizes each paper into Markdown format containing timestamp, title, authors, links, category tags, statistical information, key topics, research methods, charts (attempted extraction), and original abstract.

Content Organization: Uses a time-hierarchical directory structure (e.g., 2025/september/02-09-2025_14-30.md), making it easy to browse by time or directly access content from specific dates. Technical Features:

• Automation: Unattended operation via GitHub Actions;
• Structured Readability: Uses emojis and templates to enhance content readability;
• Multi-dimensional Classification: Covers mainstream directions like ML/AI, NLP, CV, RL;
• Open-source Collaboration: MIT license, supports community PR contributions.

The project is suitable for multiple roles:

• Researchers: Quickly filter papers that need in-depth reading, improving literature research efficiency;
• Engineers: Obtain the latest model architectures, training techniques, and application cases;
• Students: Cultivate sensitivity to academic literature and build domain cognition;
• Technical Managers: Track industry trends and assist in technical roadmap planning.

Limitations:

• Limited coverage (mainly arXiv and a few top conferences);
• Automated abstracts lack the depth of manual interpretation;
• Limited success rate in chart extraction;
• No personalized recommendation function. Improvement Directions: Expand data sources, introduce large language models to generate richer abstracts, implement personalized push, optimize chart extraction technology, etc.

Daily Paper Update is a concise and practical open-source tool that solves the pain points of academic literature tracking through automation technology, helping researchers free themselves from massive papers and focus on in-depth reading and research. For practitioners who want to keep up with AI cutting-edge trends, it is a resource worth paying attention to and using.