In the rapid proliferation of online news content has made it increasingly difficult for readers to keep up with the vast amount of information. To address this challenge, this paper proposes an AI-based news summarizer and categorizer that automates the process of extracting key insights and classifying news articles into predefined categories. The system leverages Natural Language Processing (NLP) techniques, such as BERT and TF-IDF, for feature extraction, sentiment analysis, and text categorization. The summarization process integrates extractive and abstractive methods to generate concise and informative summaries while preserving the core message. Additionally, the categorization module uses supervised learning algorithms to classify news articles across diverse topics like politics, sports, technology, and health. Evaluation on real-world datasets demonstrated that the proposed model achieves high accuracy in categorization and generates coherent, human-like summaries.The system enhances information retrieval by reducing reading time and providing organized access to news content. This research contributes to the advancement of automated news processing systems, improving user experience by delivering timely and personalized news summaries.
Resume Builder, Natural Language Processing (NLP), Machine Learning, News summarization, Text Categorization, Artificial Intelligence (AI)
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