Summarization for News Article Using Unsupervised Learning Techniques

Text summarization is one of the important tasks in Natural Language Processing, which involves shortening of information while retaining meaning. A well-condensed summary includes key details, which is grammatically correct, and free of redundancy. With rapid growth in Internet, there is vast amount of unstructured information. This demands automatic text summarization across various fields like academia, business, and search engines. Our paper develops a text summarization system for news articles in politics, sports, and reviews. It compares summaries while maintaining the original meaning, by criteria for evaluation. This will help in enhancing productivity, reducing errors in extracting insights from English and regional language data. This extends across education, commerce, and online search platforms, offering efficient entry to valuable understandings from the complex web of data. Through evaluation, we would elevate text summarization in English and regional language for better knowledge understanding a usage.