gvhemanth / transformers-based-text-summarization

Revolutionize text summarization with this Transformer model, leveraging state-of-the-art techniques. Trained on news articles, it produces concise summaries effortlessly. Explore cutting-edge capabilities for your summarization needs.

Transformer-based Text Summarization

Overview:

This project implements a text summarization model using the Transformer architecture. The model is trained on a dataset of news articles and their corresponding headlines. The goal is to generate concise and relevant summaries for given input articles.

Prerequisites:

• Python 3.x
• TensorFlow 2.x
• Openpyxl

Installation:

1. Clone the repository:

git clone https://github.com/GVHemanth/Transformers-based-Text-Summarization.git
cd transformer-text-summarization

2. Install dependencies:

pip install openpyxl --quiet

3. Download the dataset:

   • Download the dataset (news articles and headlines) and place it in the appropriate directory. Ensure the file is in the required format (e.g., Inshorts Cleaned Data.xlsx).

Usage:

1. Run the main notebook:

python transformer_summarization.ipynb

2. Monitor training progress:

   • The script will train the Transformer model for a specified number of epochs. You can adjust hyperparameters in the script or through command-line arguments.

3. Evaluate the model:

   • After training, the script provides an example of how to use the trained model to generate summaries for new input articles.

Customization:

• Adjust hyperparameters such as num_layers, d_model, num_heads, dff, and dropout_rate in the script to experiment with different configurations.

• Modify the training loop to suit your specific use case or integrate the model into your applications.

Results:

• The model's performance can be evaluated by comparing the generated summaries with the actual headlines from the dataset.

Model Checkpoints:

• Model checkpoints will be saved during training in the "checkpoints" directory. You can use these checkpoints to restore the trained model.

Contributing:

• Contributions are welcome! Feel free to submit issues or pull requests.

License:

• This project is licensed under the MIT License - see the LICENSE file for details.