Text Summarization using TextRank Algorithm

Abstract

Text summarization is crucial for managing the enormous amount of textual data that is presently accessible. It seeks to automatically provide concise summaries that accurately represent the key points from the original content. The TextRank algorithm, which uses graph-based ranking approaches to discover significant sentences in the text, is a well-known method for extractive summarization. An overview of the TextRank text summarising approach is given in this work. We go over the system's main activities, such as text preprocessing, creating a similarity matrix based on word overlap, using the TextRank algorithm to rate sentences, and choosing the phrases with the highest rankings to make up the summary. This paper provides an in-depth overview of text summarization using this approach and provides insights into the application, evaluation, and potential extensions of the TextRank algorithm.