Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Statistics-Based Summarization - Step One: Sentence Compression
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
Sentence ordering in multidocument summarization
HLT '01 Proceedings of the first international conference on Human language technology research
An overview of DR-LINK and its approach to document filtering
HLT '93 Proceedings of the workshop on Human Language Technology
Summarization: (1) using MMR for diversity - based reranking and (2) evaluating summaries
TIPSTER '98 Proceedings of a workshop on held at Baltimore, Maryland: October 13-15, 1998
Multiple & single document summarization using DR-LINK
TIPSTER '98 Proceedings of a workshop on held at Baltimore, Maryland: October 13-15, 1998
A system for query-specific document summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Extractive summarization using inter- and intra- event relevance
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
Multi-document summarization by graph search and matching
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Web queries often give rise to a lot of documents and the user is overwhelmed by the information. Query-specific extractive summarization of a selected set of retrieved documents helps the user to get a gist of the information. The current extractive summary generation systems focus on extracting query-relevant sentences from the documents. However, the selected sentences are presented either in the order in which the documents were considered or in the order in which they were selected. This approach does not guarantee a coherent summary. In this paper, we propose incremental integrated graph to represent the sentences in a collection of documents. Sentences from the documents are merged into a master sequence to improve coherence and flow. The same ordering is used for sequencing the sentences in the extracted summary. User evaluations indicate that the proposed technique markedly improves the user satisfaction with regard to coherence in the summary.