Empirical Analysis of Single and Multi Document Summarization using Clustering Algorithms

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...

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Bibliographic Details
Published in:Engineering, technology & applied science research technology & applied science research, 2018-02, Vol.8 (1), p.2562-2567
Main Authors: Bewoor, M. S., Patil, S. H.
Format: Article
Language:English
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Summary: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.
ISSN:2241-4487
1792-8036
DOI:10.48084/etasr.1775