PDF Section Summarizer with LLM

Intelligent Document Analysis & Summarization Tool

Overview

This Python-based tool automatically processes large PDF documents, identifies sections based on text formatting, and generates intelligent summaries using a Local Large Language Model (LLM). The summaries are then compiled into a well-structured Word document.

Features

• PDF text extraction with formatting awareness
• Automatic section detection based on font size
• Intelligent text summarization using Local LLM
• Word document generation with formatted summaries
• Special character handling for API compatibility
• Progress tracking during processing

Requirements

PyMuPDF>=1.18.0
python-docx>=0.8.11
subprocess
json

Usage

from pdf_summarizer import read_pdf_and_summarize

# Basic usage
read_pdf_and_summarize("input.pdf", "output.docx")

# Start from specific page
read_pdf_and_summarize("input.pdf", "output.docx", start_page=5)

API Configuration

The tool requires a local LLM API running on port 1234., you could also use an external API, but a local one is preferred because of costs because we are working with very big pdf files.

Text Processing Rules

• Sections are identified by font size > 13
• Minimum 35 words required for summarization
• Text chunks limited to 1900 words per API call so you are not exceeding with your tokens the context wimdow

Error Handling

• JSON decode error management
• API communication error handling
• Empty response handling
• Unicode character sanitization

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

#DocumentProcessing #MachineLearning #PDF #Python #LLM

Note: Make sure your local LLM API is properly configured before running the tool, i use llmstudio