KEY PHRASE-BASED AUTOMATIC TEXT DOCUMENT SUMMARIZATION

In NLP and information retrieval, summarization of text is an important function in summarizing long documents into readable, concise summaries. The process is especially applicable in the analysis of long texts like research papers, legal briefs, and news articles, where important information needs to be pulled out. The nature of summarization is to preserve meaning in the original text while reducing its length. This research focuses on two primary summarization methods: extractive and abstractive. Extractive methods pull out important sentences from the original text, while abstractive methods generate new sentences to summarize. The research focuses on three top algorithms: TextRank, Seq2Seq, and BART. TextRank, a graph-based algorithm, is best suited for short texts. Seq2Seq, a deep learning-based algorithm, produces highly accurate summaries by understanding contextual nuances. BART, a transformer-based model, performs exceptionally well in benchmark testing. The ROUGE score-based evaluation points out the algorithms' ability to generate coherent and relevant summaries. These findings illustrate the capability of advanced summarization techniques to aid information processing, making them important tools for researchers, professionals, and the public in need of effective access to important information.