Text Summarization Project 📝

Welcome to the Text-Summarization repository! This project aims to build a robust text summarization model using advanced NLP techniques and leveraging the mT5 model for multilingual summarization tasks.

📖 Project Overview

Text summarization is the process of creating a shortened version of a document that retains its key points. This project implements extractive and abstractive summarization methods using state-of-the-art models like mT5 and evaluates performance with ROUGE scores.

Key Features

• Abstractive Summarization using mT5.
• ROUGE metric evaluation for summary quality.
• Multilingual support for summarization tasks.

🛠️ Technologies Used

• Python
• mT5 Model: Multilingual version of T5 (Text-to-Text Transfer Transformer).
• ROUGE: Metric to evaluate summarization performance.
• Hugging Face Transformers: For model implementation.
• Pandas: For data handling and manipulation.
• Jupyter Notebook: For coding and experimenting with the models.

🚀 How to Run the Project

Prerequisites

Ensure you have the following installed:

• Python 3.7+
• Jupyter Notebook
• Hugging Face Transformers
• Pandas

Installation

Clone the repository:

git clone https://github.com/dhruv7539/Text-Summarization.git cd Text-Summarization

Running the Code

Open mt5forsummary.ipynb in Jupyter Notebook or JupyterLab. Run the notebook to preprocess the dataset and generate text summaries using the mT5 model. For evaluating the generated summaries using ROUGE scores, open and run mt5forrouge.ipynb.

📊 Results

The summarization model provides high-quality summaries for [list languages], and performance is evaluated using the ROUGE metric, showcasing significant improvements over traditional methods.

🧪 Testing

For testing the performance and accuracy of the model, ensure you run the evaluation notebook:

bash Copy code pytest tests/

🛠️ Future Improvements

Add more languages for multilingual support. Enhance the model's efficiency for long documents. Explore alternative evaluation metrics to complement ROUGE.

##💡 Contributing Contributions are welcome! Feel free to:

Fork the repository. Create a new branch (git checkout -b feature/new-feature). Commit your changes (git commit -m 'Add feature'). Push to the branch (git push origin feature/new-feature). Open a Pull Request.

📜 License

This project is licensed under the MIT License. See the LICENSE file for more details.

👤 Contact Author: Dhruv Email: [Your Email]