Single Document Text Summarization Using Clustering Approach Implementing for News Article

Abstract

Text summarization is an old challenge in text mining but in dire need of researcher's attention in the areas of computational intelligence, machine learning and natural language processing. We extract a set of features from each sentence that helps identify its importance in the document. Every time reading full text is time consuming. Clustering approach is useful to decide which type of data present in document. In this paper we introduce the concept of k-mean clustering for natural language processing of text for word matching and in order to extract meaningful information from large set of offline documents, data mining document clustering algorithm are adopted.

Review and Comparative Analysis of Topic Identification Techniques

“…In the proposed approach topic correlation and future, forecasting is not done. Pankaj Bhole and A. J. Agrawal (2014) proposed an application to automatically generate short news from a large article.It involves following steps: Pre-processing, Sentence clustering (using K-means), Cluster ordering (based on the number of important words it contains), Representative sentence selection (ranking for most informative sentences) and summary generation (ordered list of sentences from clusters) [39]. The proposed technique is for a single document which summarizes the text and does not consider multiple documents, also future forecasting is not done and there is no correlation.…”