A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Event tracking based on domain dependency
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
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
A summarization system for Chinese news from multiple sources
Journal of the American Society for Information Science and Technology
Identifying topics by position
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A multilingual news summarizer
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Information fusion in the context of multi-document summarization
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Text summarization using a trainable summarizer and latent semantic analysis
Information Processing and Management: an International Journal - Special issue: An Asian digital libraries perspective
From single to multi-document summarization: a prototype system and its evaluation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Probabilistic text structuring: experiments with sentence ordering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Robust temporal processing of news
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
An NTU-approach to automatic sentence extraction for summary generation
TIPSTER '98 Proceedings of a workshop on held at Baltimore, Maryland: October 13-15, 1998
Applying machine learning to Chinese temporal relation resolution
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A formal model for information selection in multi-sentence text extraction
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Cross-document event clustering using knowledge mining from co-reference chains
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Inferring strategies for sentence ordering in multidocument news summarization
Journal of Artificial Intelligence Research
Clustering and visualization in a multi-lingual multi-document summarization system
ECIR'03 Proceedings of the 25th European conference on IR research
Significant sentence extraction by euclidean distance based on singular value decomposition
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Relation Discovery from Thai News Articles Using Association Rule Mining
PAISI '09 Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics
Understanding a celebrity with his salient events
AMT'10 Proceedings of the 6th international conference on Active media technology
Text summarisation in progress: a literature review
Artificial Intelligence Review
Cluster labeling for multilingual scatter/gather using comparable corpora
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
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Summary generation for multiple documents poses a number of issues including sentence selection, sentence ordering, and sentence reduction over single-document summarization. In addition, the temporal resolution among extracted sentences is also important. This article considers informative words and event words to deal with multidocument summarization. These words indicate the important concepts and relationships in a document or among a set of documents, and can be used to select salient sentences. We present a temporal resolution algorithm, using focusing time and coreference chains, to convert Chinese temporal expressions in a document into calendrical forms. Moreover, we consider the last calendrical form of a sentence as a sentence time stamp to address sentence ordering. Informative words, event words, and temporal words are introduced to a sentence reduction algorithm, which deals with both length constraints and information coverage. Experiments on Chinese-news data sets show significant improvements of both information coverage and readability.