/MACHINE LEARNING
Text Summarization
Implemented extractive (spaCy) and abstractive (Transformers) summarization; fine-tuned models and evaluated on news articles.
- Tools & technologies
- NLP, Transformers, BERT, spaCy
- Data
- Source details to be added
- Explore
- View GitHub
A look at the project
An advanced NLP project implementing both extractive and abstractive text summarization techniques.
Approach:
- Extractive summarization using spaCy
- Abstractive summarization with Transformers
- Model fine-tuning for domain-specific content
- Evaluation on news articles dataset
Technologies:
- spaCy for natural language processing
- Hugging Face Transformers
- BERT for text understanding
- Python for implementation
Key Achievements:
- Implemented multiple summarization approaches
- Fine-tuned pre-trained models
- Comprehensive evaluation metrics
- Optimized for news article summarization
Results:
- High-quality extractive summaries
- Coherent abstractive summaries
- Improved processing speed
- Domain-specific optimization
