AI-Powered Abstractive Text Summarization for Large-Scale Data Process

In the age of information, the huge amount of data that is produced every day will remain unusual for humans unless it is made available with new tools and technologies. Abstractive Text Summarization [ATS] tries to get the most essential content of a text corpus and compress it to a shorter text while keeping its meaning and maintaining its semantic and grammatical correctness. Increases in the volume of textual data available online exponentially raise new difficulties in terms of both accuracy and speed in locating relevant information. Summarization is a useful technique for making more effective use of the vast amounts of information available to us on the Internet and in other archives. Summarization of text refers to reducing the intricacy of expressions for a summary without changing their meaning. Expert manual summary is an arduous and time-consuming task. A large quantity of knowledge would be too overwhelming for anyone to obtain, read, and apply. Therefore, in this context, it is both necessary and helpful to generate summaries.