SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
An intelligent summarization system based on cognitive psychology
Information Sciences: an International Journal
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
Extractive spoken document summarization for information retrieval
Pattern Recognition Letters
Extracting 5W1H event semantic elements from Chinese online news
WAIM'10 Proceedings of the 11th international conference on Web-age information management
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
COMPENDIUM: a text summarization system for generating abstracts of research papers
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
Atomic event semantic roles and chinese instances analysis
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
Editorial: COMPENDIUM: A text summarization system for generating abstracts of research papers
Data & Knowledge Engineering
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Event-based summarization extracts and organizes summary sentences in terms of the events that the sentences describe. In this work, we focus on semantic relations among event terms. By connecting terms with relations, we build up event term graph, upon which relevant terms are grouped into clusters. We assume that each cluster represents a topic of documents. Then two summarization strategies are investigated, i.e. selecting one term as the representative of each topic so as to cover all the topics, or selecting all terms in one most significant topic so as to highlight the relevant information related to this topic. The selected terms are then responsible to pick out the most appropriate sentences describing them. The evaluation of clustering-based summarization on DUC 2001 document sets shows encouraging improvement over the well-known PageRank-based summarization.