Fine-Tuning BART for News Summarization
Fine-Tuning BART for News Summarization
Overview
An abstractive text summarization system built by fine-tuning the BART (Bidirectional and Auto-Regressive Transformers) model on 300k+ news articles. The project focuses on generating concise, human-readable summaries from lengthy news articles. Published at Pokhara Engineering College Journal.
Key Features
- Fine-tuned BART model for optimal summarization performance
- Trained on XSum and CNN/DailyMail datasets (300k+ articles)
- Achieves strong ROUGE scores on both benchmarks
- Generates abstractive summaries that capture key information
- Natural language understanding and generation capabilities
Technologies Used
- BART (Transformers)
- Python
- PyTorch/TensorFlow
- Hugging Face Transformers
Datasets
- XSum: Extreme summarization dataset
- CNN/DailyMail: News article summarization dataset
Applications
- News aggregation platforms
- Content curation systems
- Automated report generation
- Quick information extraction from long articles