Context Sensitive Text Summarization using K Means Clustering Algorithm
Harshal J. Jain1, M. S. Bewoor2, S. H. Patil3
1Harshal J. Jain, Department of computer Engineering, Bharati Vidyapeeth College of Engineering, Pune, Maharashtra, India.
2Prof. M. S. Bewoor, Asst. Professor, Department of computer Engineering, Bharati Vidyapeeth College of Engineering, Pune, Maharashtra, India.
3Dr. S. H. Patil, Head, Department of computer Engineering, Bharati Vidyapeeth College of Engineering, Pune, Maharashtra, India.
Manuscript received on April 11, 2012. | Revised Manuscript received on April 14, 2012. | Manuscript published on May 05, 2012. | PP: 301-304 | Volume-2 Issue-2, May 2012 . | Retrieval Number: B0606042212/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The field of Information retrieval plays an important role in searching on the Internet. Most of the information retrieval systems are limited to the query processing based on keywords. In the information retrieval system matching of words with huge data is core task. Retrieval of the relevant natural language text document is of more challenging. In this paper we introduce the concept of OpenNLP tool for natural language processing of text for word matching. And in order to extract meaningful and query dependent information from large set of offline documents, data mining document clustering algorithm are adopted. Furthermore performance of the summary using OpenNLP tool and clustering techniques will be analysed and the optimal approach will be suggested.
Keywords: K means algorithm, Document graph, Context sensitive text summarization.