Automated Hugging Face Paper Summary Fetching & Categorization Workflow

Product Name: Automated Hugging Face Paper Summary Fetching & Categorization Workflow

Product Description:

This automated workflow brings you an efficient process for fetching, analyzing, categorizing, and publishing Hugging Face paper summaries. By integrating n8n and OpenAI, you can easily fetch the latest paper summaries every weekday at 8 AM, analyze their content, automatically store them in Notion, and push the relevant information to Slack, helping you stay updated with the latest research trends.

Features:

1. Scheduled Fetching: Automatically triggers every weekday at 8 AM to fetch the latest papers from Hugging Face.
2. Automated Analysis: Uses OpenAI to deeply analyze paper summaries and extract key information.
3. Data Deduplication: Automatically checks if the same paper summary already exists in Notion to avoid duplicates.
4. Categorization: Automatically categorizes the paper content to keep your knowledge base well-organized.
5. Slack Notifications: Sends real-time paper summaries to Slack, ensuring your team stays updated with the latest research.
6. Notion Storage: Automatically stores analyzed paper summaries and related information in Notion for easy future reference.

Workflow:

1. Trigger: Automatically triggers every weekday at 8 AM.
2. Fetch Paper: Requests Hugging Face to fetch the paper's detailed page.
3. Duplication Check: Checks if the paper summary already exists in Notion.
4. Data Analysis & Categorization:
   • Fetches the detailed summary.
   • Uses OpenAI for automatic content analysis and categorization.
5. Storage & Notifications:
   • Stores the summary in Notion.
   • Pushes the paper summary and key information to Slack channels.

Example Slack Notification:

Title: Evolving Deeper LLM Thinking
Core Introduction: The paper presents Mind Evolution, an evolutionary search strategy for scaling inference time compute in Large Language Models. Mind Evolution generates, recombines, and refines candidate responses using a language model, avoiding the need to formalize the underlying inference problem when a solution evaluator exists. It is applied to natural language planning tasks and demonstrates significant performance improvements over existing approaches.
Keywords: Large Language Models, Evolutionary Search, Inference Strategies, Natural Language Planning, Optimization
Key Data and Results: Mind Evolution achieves over 98% problem-solving accuracy on the TravelPlanner and Natural Plan benchmarks using Gemini 1.5 Pro, significantly outperforming other strategies such as Best-of-N and Sequential Revision while maintaining controlled inference costs.
Technical Details: Mind Evolution utilizes an evolutionary search mechanism to generate, recombine, and refine responses. It relies on a language model for inference tasks, eliminating the need for a formal solver when a solution evaluator is available. Benchmarks include TravelPlanner and Natural Plan tasks.
Classification: Artificial Intelligence (AI), Natural Language Processing (NLP), Optimization and Search Strategies, Planning and Decision Making
URL: https://huggingface.co/papers/2501.09891

Requirements:

• Open AI Token ,Notion Token and Slack Token are required to enable automatic storage and notifications.
• You need to register and import this workflow into the n8n Cloud service. Registration link: n8n Cloud Registration.

How to Use:

1. Register and log in to the n8n Cloud service
2. Configure your Notion and Slack Tokens.
3. Import and activate this workflow.
4. Every weekday, the workflow will run automatically, ensuring you never miss an important paper.

With this automated workflow, you can stay updated on academic trends more efficiently, save time, and enhance your research and learning productivity.