The News Article Summarizer is an application that leverages Transformer models to generate concise and accurate summaries of large news articles. The model has been trained on over 20,000 news articles to ensure high-quality outputs. The application is deployed on an AWS EC2 instance, providing a user-friendly web interface for article summarization. The text summariser is hosted at http://18.217.224.109/
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High-Quality Summaries: Uses state-of-the-art Transformer models to create accurate and concise summaries.
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User-Friendly Interface: Intuitive web interface built with Flask for easy usage.
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AWS Hosting: Deployed on an AWS EC2 instance for reliable and scalable access.
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Real-Time Processing: Summarizes articles in real-time with low latency.
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Frontend: HTML, CSS
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Backend: Flask (Python)
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Model: Transformer-based model
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Hosting: AWS EC2 instance
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Additional Libraries: Pandas (data processing)
To set up the project locally, ensure you have the following installed:
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Python 3.8 or later
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Pip (Python package installer)
git clone https://github.com/kishalay2002/Text-Summarizer.git
cd Text-Summarizer
pip install -r requirements.txt
python app.py
The application will be accessible at http://localhost:5000
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Navigate to the application URL.
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Paste the news article text into the input box.
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Click "Summarize" to generate a summary.
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The summarized text will be displayed below the input box.
"Artificial intelligence (AI) is transforming industries around the globe. Companies are investing heavily in AI technologies to enhance efficiency and drive innovation."
"AI is transforming industries, with companies investing to enhance efficiency and innovation."
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Add support for summarizing URLs directly.
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Improve the UI for better user experience.
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Implement multilingual summarization capabilities.
Contributions are welcome! Please fork the repository and submit a pull request with your changes.
This project is licensed under the MIT License. See the LICENSE file for details.