TEXT SUMMARIZATION SYSTEM USING NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING

Abstract

Information overload, caused by the rapid growth of the Internet, has become a significant issue. The abundance of information available online necessitates the simplification of relevant content through summarization. Manual summarization of large amounts of text is a daunting task for humans, leading to a demand for more advanced and powerful summarization techniques. Researchers have been striving to enhance the quality of machine-generated summaries to match those created by humans since the 1950s. This study presents a comprehensive analysis of text summarization, covering various aspects such as summarization approaches, techniques, standard datasets, evaluation metrics, and future research prospects.The study delves into two widely accepted approaches in text summarization: extractive and abstractive. These approaches are explored in detail, highlighting their strengths and limitations. To facilitate comparison and replication of findings, the evaluation of summaries and the development of reusable resources and infrastructure are crucial, fostering healthy competition and driving improvements in summarization outcomes. The study also discusses different evaluation methods employed to assess the quality of generated summaries.In conclusion, this study provides valuable insights into the field of text summarization. It examines the current state of the art, including summarization techniques, datasets, evaluation metrics, and future research opportunities. By summarizing vast amounts of information effectively, text summarization can alleviate the burden of information overload. The challenges and opportunities discussed in this study offer a roadmap for researchers interested in advancing text summarization techniques and contributing to this evolving field.