Heart Disease Prediction using Machine Learning

The Web Article Summarizer is an advanced NLP-based application that leverages state-of-the-art transformer models, including BART for abstractive summarization and BERT for contextual understanding, combined in a dual-encoder architecture to generate accurate and coherent summaries from lengthy articles. Built using the Flask framework, the system features a scalable RESTful API that enables seamless integration with web and mobile platforms, while its multi-stage preprocessing pipeline ensures optimal text normalization and feature extraction. Evaluated using ROUGE metrics, the solution demonstrates superior performance in maintaining content integrity and domain-specific terminology handling compared to traditional methods. Designed for researchers, students, and professionals, this tool addresses information overload challenges by delivering concise yet comprehensive summaries, with future enhancements planned for multilingual support via mBART, cloud deployment for real-time processing, and JWT authentication for secure API access, making it a versatile and powerful solution for automated text summarization across diverse applications.