An Approach of Categorization and Summarization of News using Topic Modeling

Abstract

The increasing demand/availability of online content has triggered intensive research in the automatic text summarization. Text summarization is the process of removing less useful text from the document to find the required news quickly. Likewise, text summarization, news summarization is also the process of picking the news content which is most important in perspective of online readers and gives the clear idea of the proposed news. Various traditional algorithms are available that can be used to summarize the text. In this paper, an automatic text summarizer using extractive summarization approach is proposed and implemented by considering topic modelling for categorization of news content and text rank algorithm for summarization. In order to evaluate accuracy, F-measure and recall of the produced summary, various machine learning algorithms are applied. The result produced 99.8% of accuracy using topic modelling over K-means clustering.