Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
WordNet: a lexical database for English
Communications of the ACM
NewsInEssence: summarizing online news topics
Communications of the ACM - The digital society
Enhancing Text Clustering Using Concept-based Mining Model
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Summarizing large document sets using concept-based clustering
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Design and development of a concept-based multi-document summarization system for research abstracts
Journal of Information Science
Clustering Documents Using a Wikipedia-Based Concept Representation
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Multi-document summarization by sentence extraction
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
A WordNet-Based Semantic Model for Enhancing Text Clustering
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Reducing redundancy in multi-document summarization using lexical semantic similarity
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
Document update summarization using incremental hierarchical clustering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
An algorithm for finding document concepts using semantic similarities from WordNet ontology
International Journal of Computational Vision and Robotics
A hierarchical document clustering environment based on the induced bisecting k-means
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
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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.