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--- |
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title: News Article Summarizer |
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emoji: 📰 |
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colorFrom: blue |
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colorTo: indigo |
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sdk: gradio |
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sdk_version: 5.9.1 |
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app_file: app.py |
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pinned: false |
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license: mit |
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--- |
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# 📰 News Article Summarizer |
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A powerful and efficient news article summarization tool powered by BART-Large-CNN model. This application automatically extracts and summarizes news articles from URLs, making it easier to quickly grasp the key points of any news article. |
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## 🚀 Features |
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- **Smart Article Extraction**: Automatically extracts article content from news URLs |
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- **Advanced Summarization**: Uses BART-Large-CNN model for high-quality summaries |
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- **Chunk Processing**: Handles long articles by processing them in chunks |
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- **Clean Output**: Removes unwanted elements like ads and navigation for better results |
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- **User-Friendly Interface**: Simple Gradio interface for easy interaction |
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## 🛠️ Technology Stack |
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- **Python**: Core programming language |
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- **BART-Large-CNN**: State-of-the-art summarization model |
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- **Gradio**: Web interface framework |
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- **BeautifulSoup4**: HTML parsing and content extraction |
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- **PyTorch**: Deep learning framework |
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- **Transformers**: Hugging Face transformers library |
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## 📋 Requirements |
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``` |
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gradio==5.9.1 |
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transformers |
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torch |
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beautifulsoup4 |
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requests |
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nltk |
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``` |
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## 🚀 Getting Started |
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1. **Install Dependencies**: |
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```bash |
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pip install -r requirements.txt |
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``` |
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2. **Run the Application**: |
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```bash |
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python app.py |
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``` |
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3. **Use the App**: |
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- Open the provided URL in your browser |
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- Paste a news article URL |
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- Wait for the summary (processing time depends on article length) |
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- Get your concise summary! |
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## 💡 How It Works |
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1. **Article Extraction**: |
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- Fetches article content from the provided URL |
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- Removes unwanted elements (ads, navigation, etc.) |
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- Extracts main article text |
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2. **Text Processing**: |
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- Splits long articles into manageable chunks (1024 tokens each) |
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- Cleans and prepares text for summarization |
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3. **Summarization**: |
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- Uses BART-Large-CNN model for each chunk |
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- Combines summaries for a coherent final output |
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- Provides clean, readable summaries |
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## ⚠️ Notes |
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- Processing time varies based on article length |
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- Look for "Running..." indicator while processing |
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- Wait patiently for best results |
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- Model can be changed to T5 or GPT-2 for different results |
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## 🔄 Example Usage |
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```python |
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# Example URLs: |
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https://www.bbc.com/sport/football/articles/cvgxmzy86e4o |
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https://globalsouthworld.com/article/biden-approves-571-million-in-defense-support-for-taiwan |
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``` |
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## 🤝 Contributing |
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Feel free to: |
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- Open issues |
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- Suggest improvements |
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- Submit pull requests |
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## 📝 License |
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This project is open source and available under the MIT License. |
