PageRank–Based Extractive Text Summarization

Abstract

At present text summarization is one of the important aspects of data science. As the bulk of data is available on the Internet, also the number of web pages is available for the same. Most of the time we find that with irrelevant data sometimes fraudulent data is also available. Fraudulent data may be identifiable when going through whole documents of that web page, but the effort results in a waste of time and money. To overcome these problems, text summarization is a robust solution. PageRank is one of the efficient algorithms for text summarization. By considering the vector matrix, we can verify which page is important. For the same, we used a mathematical model for calculating iteration and find the best score of pages.