Text summarization automatically creates a shorter version of one or more text documents. It is an effective way of finding relevant information from large set of documents. Text summarization techniques are categorized as Extractive summarization and Abstractive summarization. Extractive summarization methods evaluate text summarization by selecting sentences present in documents according to predefined set of rules. Abstractive summaries attempt to improve the coherence among sentences by eliminating redundancies and clarifying the content of sentences. It should also extract the information is such a way that the content would be in the interest of the user. In this paper we used tokenization for preprocessing of statements then calculate TF/IDF for feature extraction, K-means clustering to generate clusters containing high frequency statements and then NEWSUM algorithm for weighing of statements that are used for relevant content summarization. We also present experimental results on a number of real data sets in order to illustrate the advantages of using proposed approach

Text Mining, Text Summarization, Clustering, extractive summary, information extraction

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