Empirical Analysis of Single and Multi Document Summarization using Clustering Algorithms

M. S. Bewoor, S. H. Patil


The availability of various digital sources has created a demand for text mining mechanisms. Effective summary generation mechanisms are needed in order to utilize relevant information from often overwhelming digital data sources. In this view, this paper conducts a survey of various single as well as multi-document text summarization techniques. It also provides analysis of treating a query sentence as a common one, segmented from documents for text summarization. Experimental results show the degree of effectiveness in text summarization over different clustering algorithms.


Text mining, text summarization, clustering

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A. Kaushik, S. Naithani, “A Comprehensive Study of Text Mining Approach”, International Journal of Computer Science and Network Security, Vol. 16, No. 2, pp. 69–76, 2016

R. Varadarajan, V. Hristidis, “A system for query-specific document summarization”, 15th ACM international conference on Information and knowledge management, pp. 622-631, 2006

J. Goldstein, V. Mittal, J. Carbonell, M. Kantrowitz, “Multi-Document Summarization By Sentence Extraction”, NAACL-ANLPWorkshop on Automatic summarization, Vol. 4, pp. 40–48, 2000

Y. J. Kumar, N. Salim, “Automatic multi document summarization approaches”, Journal of Computer Science, Vol. 8, No. 1, pp. 133–140, 2012

S. Gholamrezazadeh, M. A. Salehi, B. Gholamzadeh, “A comprehensive survey on text summarization systems”, 2nd International Conference on Computer Science and its Applications, pp. 1-6, 2009

M. Steinbach, G. Karypis, V. Kumar, “A Comparison of Document Clustering Techniques”, KDD workshop on text mining, Vol. 400, No. 1, pp. 525-526, 2000

D. Vidyadharan, A. CR “A Query Based Summerizer Based on the Context ” International Journal of Science and Research, Vol. 4, No. 5, pp. 3018-3020, 2015

T. K. Fan, C. H. Chang, “Exploring Evolutionary Technical Trends from Academic Research Papers”, Eighth IAPR International Workshop on Document Analysis Systems, pp. 574-581, 2008

D. Y. Sakhare, R. Kumar, “Syntactic and Sentence Feature Based Hybrid Approach for Text Summarization”, Internation Information Technology and Computer Science, Vol. 2014, No. 3, pp. 38–46, 2014

M. N. Ingole, M. S. Bewoor, S. H. Patil, “Text Summarization using Expectation Maximization Clustering Algorithm”, International Journal of Engineering Research and Applications, Vol. 2, No. 4, pp. 168–171, 2012

V. J. Roma, M. S. Bewoor, S. H. Patil, “Automation Tool for Evaluation of NLP based Text Summary Generated through Summarization and Clustering Techniques by Quantitative and Qualitative Metrics”, International Journal of Computer Engineering and Technology, Vol. 4, No. 3, pp. 77–85, 2013

M. K. Gawali, M. S. Bewoor, S. H. Patil, “Review : Performance Evaluator of Optimized Text Summary Algorithm”, International Journal of Computer Science and Technology Vol. 4, No. 1, pp. 295–296, 2013

V. J. Roma, M. S. Bewoor, S. H. Patil, “Evaluator and Comparator : Document Summary Generation based on Quantitative and Qualitative Metrics for International Journal of Scientific & Engineering Research”, International Journal of Scientific & Engineering Research, Vol. 4, No. 5, pp. 1111–1115, 2013

A. Nenkova, “Automatic Text Summarization of Newswire: Lessons Learned from the Document Understanding Conference”, Association for the Advancement of Artificial Intelligence, Vol. 5, pp. 1436-1441, 2005

M. J. A. Eugster, Benchmark Experiments—A Tool for Analyzing Statistical Learning Algorithms, PhD Thesis, Ludwig-Maximilians-Universitat, 2011.

M. Hassel, Evaluation of Automatic Text Summarization, Licentiate Thesis, 2004

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