Abstract
Abstract: The millions of words or sentences on the Internet and in literally hundreds of archives—literary works, published research, legal documentation, as well as other data—have enabled Automatic Text Summarization (ATS), which has become exceedingly well known over the past 10 years. Word-based summarizing is time-consuming, affordable, and unsustainable when interacting with vast volumes of literary texts. It's why we prefer utilizing textual data snippets: it saves time, we acquire accurate data rapidly, and computer scientists have been attempting to create ATS replacements since the 1950s. In this research, we will compare alternative rating systems and offer the best summary based on the scoring methodology implemented. We will also clearly define a wide range of methods, techniques, and scoring algorithms.
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