Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
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ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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ACM Transactions on Information Systems (TOIS)
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-document summarization using sentence-based topic models
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Application of Text Summarization techniques to the Geographical Information Retrieval task
Expert Systems with Applications: An International Journal
A knowledge induced graph-theoretical model for extract and abstract single document summarization
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
PSG: a two-layer graph model for document summarization
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Similar to the traditional approach, we consider the task of summarization as selection of top ranked sentences from ranked sentence-clusters. To achieve this goal, we rank the sentence clusters by using the importance of words calculated by using page rank algorithm on reverse directed word graph of sentences. Next, to rank the sentences in every cluster we introduce the use of weighted clustering coefficient. We use page rank score of words for calculation of weighted clustering coefficient. Finally the most important issue is the presence of a lot of noisy entries in the text, which downgrades the performance of most of the text mining algorithms. To solve this problem, we introduce the use of Wikipedia anchor text based phrase mapping scheme. Our experimental results on DUC-2002 and DUC-2004 dataset show that our system performs better than unsupervised systems and better than/comparable with novel supervised systems of this area.