An NLP-powered application that automatically generates concise and meaningful summaries from long text documents using state-of-the-art Natural Language Processing techniques.
This project demonstrates how transformer-based language models can be integrated into a simple, user-friendly application for document summarization.
- Generate summaries from long text
- Fast and accurate text compression
- Supports multiple paragraph inputs
- Clean and responsive user interface
- Easy-to-use API (if applicable)
- Built using modern NLP techniques
Add screenshots or a demo GIF here.
Example:
Input
Artificial Intelligence has transformed many industries...
Output
Artificial Intelligence is transforming industries by improving automation,
decision-making, and efficiency.
- Python
- Hugging Face Transformers
- PyTorch
- TensorFlow (if used)
- BART
- T5
- Pegasus
- Tokenizers
- Flask / FastAPI (if applicable)
- HTML
- CSS
- JavaScript
- Bootstrap (if used)
text_summarize/
│
├── app.py
├── model/
├── static/
├── templates/
├── utils/
├── requirements.txt
├── README.md
└── ...
(Update this tree to match your repository.)
Clone the repository
git clone https://github.com/D-engahmed/text_summarize.gitMove into the project
cd text_summarizeCreate a virtual environment
python -m venv venv
venv\Scripts\activatepython3 -m venv venv
source venv/bin/activateInstall dependencies
pip install -r requirements.txtIf using Flask
python app.pyIf using FastAPI
uvicorn app:app --reloadOpen your browser
http://127.0.0.1:5000
or
http://127.0.0.1:8000
The summarization model is based on Transformer architecture trained for abstractive summarization.
Possible supported models include:
- facebook/bart-large-cnn
- google/pegasus-xsum
- t5-base
- t5-small
Input Text
│
▼
Preprocessing
│
▼
Tokenization
│
▼
Transformer Model
│
▼
Generated Summary
│
▼
Display Result
- PDF summarization
- DOCX support
- Multi-language summarization
- Batch summarization
- Upload documents
- API endpoint
- Docker deployment
- Model selection
- Export summary as PDF
Add screenshot here
Add screenshot here
Example
Python 3.10+
transformers
torch
flask
sentencepiece
accelerate
numpy
Install
pip install -r requirements.txtContributions are welcome.
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Fork the repository
-
Create a feature branch
git checkout -b feature/new-feature- Commit your changes
git commit -m "Add new feature"- Push
git push origin feature/new-feature- Open a Pull Request
This project is licensed under the MIT License.
Ahmed Abdallah Elkossairy
- GitHub: https://github.com/D-engahmed
- LinkedIn: https://www.linkedin.com/in/ahmed-elkossairy/
If you found this project useful, consider giving it a ⭐ on GitHub.