This repository contains the master thesis project for implementing an Automatic Text Summarization (ATS) system with transformers T5, HuggingFace, and OpusMT.
Our work consists of a comprehensive study and implementation of a novel ATS system using state-of-the-art transformer models like T5, along with HuggingFace and OpusMT for efficient natural language understanding, processing, and translation. The ultimate goal is to create an effective, high-performing text summarization system that is both abstractive and efficient.
data_exploration: Scripts and notebooks for exploring and understanding the dataset can be found here.datasets: This directory houses the training, validation, and test splits of the CNN/Daily Mail dataset.evaluation: This directory contains evaluation scripts for calculating ROUGE-1 scores and other evaluation metrics.decoding_hyperparameter_search: This directory holds scripts to determine the optimal hyperparameters for the model.nmt: This directory contains scripts related to Neural Machine Translation (NMT).plots: This directory contains various plots and figures generated during the project.t5_summarization: Contains scripts related to T5 summarization.
We appreciate contributions of all kinds. If you would like to contribute to this project, please fork the repository and submit a pull request.
This project is licensed under the MIT License - see the LICENSE.md file for details.
For any questions or suggestions, feel free to open an issue or reach out to us.