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Ollama Ebook Summary
Overview :
ollama-ebook-summary is a project that utilizes large language models (LLM) to create key point summaries for long texts. This project is particularly suitable for books in epub and pdf formats, automating the extraction of chapters and splitting them into manageable chunks of approximately 2000 tokens to enhance response granularity. The product's background stems from the creator's desire to swiftly summarize a range of books to integrate psychological theories and practices, forming a coherent argument based on this information. The main advantages of this tool include increased efficiency in content organization, support for custom query questions, and the generation of detailed summaries for each text section.
Target Users :
The target audience includes users who need to process large volumes of text, such as writers, researchers, students, or any professionals seeking to quickly extract information from lengthy texts. This tool is ideal for them because it significantly reduces the time required for manual summarization of books and long texts, thereby enhancing productivity.
Use Cases
Researchers use this tool to quickly summarize psychology books, integrating theories and experimental results from different texts.
Students leverage this tool to extract key events and dates from lengthy historical documents in preparation for exams.
Writers apply this tool to distill core ideas from their long works and construct book outlines.
Features
Automated extraction of book chapters and segmentation into small chunks: capable of processing epub and pdf formats, automatically extracting chapters and splitting them into easily manageable pieces.
Generate key point summaries: create key point notes for each text chunk, including bold headlines and terminology.
Support for custom query questions: users can ask questions about each part of the text to receive more specific information.
Support for various model usage: includes models provided by Ollama and HuggingFace, allowing users to select the appropriate model based on their needs.
Output formatted text: supports outputting in CSV or Markdown format for user convenience in further processing and viewing.
Support for long text processing: especially suitable for lengthy texts such as eBooks, capable of handling large amounts of text and generating summaries.
Customizable configuration file: users can update the configuration file to meet different summarization needs.
How to Use
1. Ensure that Python 3.11.9 is installed.
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2. Install project dependencies by running the command `pip install -r requirements.txt`.
3. Download and set up the required models using Ollama or HuggingFace.
4. Update the configuration file `_config.yaml` to set the default prompt and model.
5. Use the script `python3 book2text.py ebook-name.epub` to convert the eBook into chunked CSV or TXT files.
6. Run `python3 sum.py -c ebook-name_processed.csv` to generate summaries.
7. Review the generated Markdown or CSV files to obtain summarized key points from the books.
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