Dr.K.P.Kaliyamurthie
Automated information retrieval systems are used to reduce the overload of document retrieval. There is a need to provide high quality summary in order to allow the user to quickly locate the desired information. This paper proposes a new summarization technique which considers correlated concepts i.e. terms and related terms as concepts for concept based document summarization. Related documents are grouped into same cluster by Bisecting k-means clustering algorithm. From each cluster important sentences are extracted by concept matching and also based on sentence feature score. Also we adopt a modified redundancy elimination technique which is purely based on concepts rather than terms. Experiments are carried to analyze the performance of the proposed work with the existing term based and synonyms and hypernyms based summarization techniques considering scientific articles and news tracks as data set. From the analysis it is inferred that our proposed technique gives better enhancement for the documents related to scientific terms.
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