Book Chapter Summarizer

An AI-powered tool that generates comprehensive chapter summaries and thematic analyses for books. Supports both fiction and nonfiction works with intelligent content extraction and rolling context awareness.

• Intelligent Summarization: Generates summaries that are 10-15% of original chapter length
• Thematic Analysis: Provides character dynamics, theme exploration, and narrative analysis
• Rolling Context: Maintains story/argument continuity across chapters
• Fiction & Nonfiction Support: Automatically detects book type and adapts prompts accordingly
• Long Chapter Handling: Automatically chunks and processes lengthy chapters
• EPUB Support: Works with extracted EPUB book folders

1. Clone the repository:

git clone https://github.com/pokemon918/Book_Features.git
cd Book_Features

2. Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate     # Windows

3. Install dependencies:

pip install -r requirements.txt

4. Configure API key:

cp env_example.txt .env
# Edit .env and add your OpenAI API key

Edit config.py to customize:

python summarizer.py <book_folder>

Example:

python summarizer.py the_murder_links_epub

python run_all.py

Each book should be in a folder with the following structure:

book_name_epub/
├── book.metadata          # JSON with title, authors, etc.
├── 01_Chapter_Title.txt   # Chapter files (sorted alphabetically)
├── 02_Chapter_Title.txt
├── ...
└── summaries/             # Generated output (created automatically)
    ├── 01_Chapter_Title_summary.txt
    ├── 02_Chapter_Title_summary.txt
    └── book_context.json

The .metadata file should contain:

{
  "title": "Book Title",
  "authors": ["Author Name"]
}

Each summary file contains:

Chapter Title

SUMMARY

[Detailed chapter summary - 10-15% of original length]

ANALYSIS

[Thematic analysis and character dynamics]

1. Load Book: Reads metadata and chapter files from the book folder
2. Detect Type: Identifies fiction vs nonfiction based on author/title
3. For Each Chapter:
   • Extract Elements: Characters, events, themes (fiction) or arguments, concepts, evidence (nonfiction)
   • Generate Summary: Creates detailed summary maintaining narrative flow
   • Generate Analysis: Analyzes themes and character/narrative dynamics
   • Update Context: Maintains rolling context for continuity
4. Save Output: Writes summaries and final book context

Book_Features/
├── summarizer.py      # Main processing logic
├── prompts.py         # LLM prompt templates
├── config.py          # Configuration settings
├── run_all.py         # Batch processing script
├── requirements.txt   # Python dependencies
├── env_example.txt    # API key template
└── *_epub/            # Book folders

• Python 3.10+
• OpenAI API key
• Dependencies: openai, tiktoken, python-dotenv

MIT License