NLP-Text-Summarizer

A Python-based project for abstractive text summarization using deep learning models.

Overview

This repository contains code for an abstractive text summarization system. The goal is to generate concise and coherent summaries of longer texts, capturing the most important information while rephrasing it in new sentences.

Features

• Abstractive Summarization: Generates summaries that may contain words not present in the original document.
• Deep Learning Models: Utilizes state-of-the-art neural network architectures (e.g., Transformer, LSTM) for sequence-to-sequence tasks.
• Pre-trained Models: Leverages pre-trained models for transfer learning to improve summarization quality.
• Evaluation Metrics: Includes implementations for ROUGE scores to evaluate summary quality.

Installation

git clone https://github.com/Theoplad9/NLP-Text-Summarizer.git
cd NLP-Text-Summarizer
pip install -r requirements.txt

Usage

from summarizer import Summarizer

text = """Your long input text here..."""
summarizer = Summarizer()
summary = summarizer.summarize(text)
print(summary)

Project Structure

NLP-Text-Summarizer/
├── src/
│   ├── models/
│   │   ├── __init__.py
│   │   └── transformer.py
│   ├── data/
│   │   ├── __init__.py
│   │   └── dataset.py
│   ├── utils/
│   │   ├── __init__.py
│   │   └── metrics.py
│   └── summarizer.py
├── notebooks/
│   └── exploration.ipynb
├── tests/
│   └── test_summarizer.py
├── requirements.txt
├── README.md
└── LICENSE

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for details.

License

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