Document Summarize

This AI tool summarizes text/PDFs using a local Llama-based model. It supports multiple languages, uses direct or map-reduce summarization, offers various styles, runs locally for privacy

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Document Summarization

Inspiration

The idea for this project came from the increasing need to process large volumes of text efficiently. Researchers, students, and professionals often deal with lengthy documents, and manually extracting key information is time-consuming. This inspired us to build an AI-powered summarization tool that provides concise and accurate summaries.

What It Does

The tool takes in plain text or PDF documents and generates a summary using an AI model. It supports multiple languages and allows users to choose different summarization styles, such as concise, detailed, or list-based. The system works efficiently by either summarizing the document directly or using a map-reduce technique for larger files.

How We Built It

  1. Model Selection – Used the Mistral-7B model optimized for local execution.
  2. Text Processing – Implemented RecursiveCharacterTextSplitter to handle large documents.
  3. Summarization Workflow – Developed both direct and map-reduce summarization techniques.
  4. User Interface – Created a simple and interactive Gradio-based UI.
  5. Local Execution – Ensured privacy by running all processing on the user’s machine.

Challenges We Ran Into

  • Optimizing memory usage for large documents.
  • Balancing processing speed and summary accuracy.
  • Designing a user-friendly UI for seamless interaction.

Accomplishments That We're Proud Of

  • Successfully implementing AI-powered summarization that works locally.
  • Enabling multi-language support and flexible summarization styles.
  • Creating a lightweight, efficient, and accessible tool for users.

What We Learned

  • Fine-tuning large language models for specific tasks.
  • The importance of text chunking in AI-driven document processing.
  • Effective UI/UX design principles for AI-powered applications.

What's Next for Document Summarization

  • Improving processing speed and memory efficiency.
  • Experimenting with different LLMs for better accuracy.
  • Adding support for more file formats and advanced customization options.

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