Extractive Based Single Document Text Summarization Using Clustering Approach

Abstrak

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.