Here's a clean and professional README.md file for your Text Summarizer using RAG-like Pipeline project:

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# 🧠 Text Summarizer using RAG-like Pipeline

This project is a **text summarization tool** that intelligently compresses large documents into concise summaries. It combines **semantic chunk retrieval** using Sentence-BERT and FAISS with **abstractive summarization** powered by BART, following a RAG (Retrieval-Augmented Generation) inspired architecture.

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## 🚀 Features

- 🔍 **Semantic Chunking**: Splits large text into overlapping chunks.
- 🤖 **Embedding & Retrieval**: Uses Sentence-BERT with FAISS for finding relevant chunks.
- ✨ **Abstractive Summarization**: Summarizes the most relevant content using Facebook's BART model.
- ⚡ **GPU Support**: Automatically uses GPU if available for faster performance.
- 🧩 **Modular Codebase**: Easy to customize and extend.

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## 📁 Project Structure

Text_summarizer/ ├── summarizer.py # Main pipeline script ├── text.txt # Input text file to be summarized ├── requirements.txt # Python dependencies └── README.md # Project documentation

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## 📦 Installation

1. Clone the repository:

```bash
git clone https://github.com/your-username/text-summarizer-rag.git
cd text-summarizer-rag

2. Create a virtual environment (optional but recommended):

python -m venv venv
venv\Scripts\activate  # On Windows

3. Install the dependencies:

pip install -r requirements.txt

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🛠️ Usage

1. Place your input text in the text.txt file.
2. Run the summarizer:

python summarizer.py

3. The script will output a concise summary of the input text.

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⚙️ Configuration

You can change the following constants in summarizer.py:

CHUNK_SIZE = 500             # Size of each text chunk
CHUNK_OVERLAP = 100          # Overlap between chunks
TOP_K = 5                    # Number of top relevant chunks to summarize
MAX_SUMMARY_LENGTH = 512     # Max length of generated summary
MIN_SUMMARY_LENGTH = 150     # Min length of generated summary

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🧠 Models Used

• Embedding: sentence-transformers/all-MiniLM-L6-v2
• Summarization: facebook/bart-large-cnn

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📌 To-Do / Ideas for Future

• Add Streamlit/Gradio UI
• Enable question-based summarization (true RAG-style QA)
• Support PDF or DOCX input
• Improve chunking using token-based NLP

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🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first.

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📄 License

This project is licensed under the MIT License.

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📬 Contact

For questions, feedback, or collaboration:

Varun
🔗 https://github.com/Varun0818

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