News Article Summarization

The “News Article Summarization” AI/ML project aims to develop an automated system that condenses news articles into concise summaries, allowing users to quickly grasp key points without reading lengthy texts. The project begins with collecting a diverse dataset of news articles from various sources, which is then preprocessed to remove unnecessary elements and prepare the text for analysis. Two main approaches are employed: extractive summarization, which identifies and selects key sentences, and abstractive summarization, which uses advanced models like BART or T5 to generate new, coherent summaries. Evaluation metrics, particularly ROUGE scores, are used to assess the quality of the summaries, and user feedback is gathered to refine the model further. The final product is deployed through a user-friendly web interface or API, enabling easy access to the summarization tool. Overall, this project not only enhances information accessibility but also helps users manage information overload, making it a valuable resource in today’s fast-paced information landscape.