Automatic Text Summarization Methods

Withtheexponentialincreaseindigitalinformation, the challenge of information overload has become critical. Automatic Text Summarization (ATS) offers a solution by distilling keyinformationfromlargetextsintoconcisesummaries.ThispaperexploresATSmethodologies,focusingonclassificationsbased on input type, purpose, and output type. It provides a detailed analysis of Extractive Text Summarization (ETS), Abstractive Text Summarization (ABS), and Hybrid Text Summarization (HTS). Our implemented ATS system achieves an impressive 90% accuracy, highlighting its effectiveness and reliability. By comparing techniques, datasets, and evaluation metrics, this paper identifies strengths and limitations while proposing future improvements in ATS systems.