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The Scientific World Journal
Volume 2016 (2016), Article ID 1784827, 10 pages
http://dx.doi.org/10.1155/2016/1784827
Research Article

An Automatic Multidocument Text Summarization Approach Based on Naïve Bayesian Classifier Using Timestamp Strategy

1Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Pennalur, Sriperumbudur TK 602117, India
2Department of Information Technology, RMK Engineering College, Kavaraipettai 601206, India

Received 20 October 2015; Revised 5 January 2016; Accepted 13 January 2016

Academic Editor: Juan M. Corchado

Copyright © 2016 Nedunchelian Ramanujam and Manivannan Kaliappan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Nowadays, automatic multidocument text summarization systems can successfully retrieve the summary sentences from the input documents. But, it has many limitations such as inaccurate extraction to essential sentences, low coverage, poor coherence among the sentences, and redundancy. This paper introduces a new concept of timestamp approach with Naïve Bayesian Classification approach for multidocument text summarization. The timestamp provides the summary an ordered look, which achieves the coherent looking summary. It extracts the more relevant information from the multiple documents. Here, scoring strategy is also used to calculate the score for the words to obtain the word frequency. The higher linguistic quality is estimated in terms of readability and comprehensibility. In order to show the efficiency of the proposed method, this paper presents the comparison between the proposed methods with the existing MEAD algorithm. The timestamp procedure is also applied on the MEAD algorithm and the results are examined with the proposed method. The results show that the proposed method results in lesser time than the existing MEAD algorithm to execute the summarization process. Moreover, the proposed method results in better precision, recall, and -score than the existing clustering with lexical chaining approach.