Efficient text summarization using lexical chains
Proceedings of the 5th international conference on Intelligent user interfaces
Temporal summaries of new topics
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Computational Linguistics - Summarization
Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
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
Sub-event based multi-document summarization
HLT-NAACL-DUC '03 Proceedings of the HLT-NAACL 03 on Text summarization workshop - Volume 5
BioChain: lexical chaining methods for biomedical text summarization
Proceedings of the 2006 ACM symposium on Applied computing
The use of domain-specific concepts in biomedical text summarization
Information Processing and Management: an International Journal
Event-Based Summarization Using Time Features
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Sentence extraction using time features in multi-document summarization
AIRS'04 Proceedings of the 2004 international conference on Asian Information Retrieval Technology
Atomic event semantic roles and chinese instances analysis
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
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In this paper, we investigate whether temporal relations among event terms can help improve event-based extractive summarization and text cohesion of machine-generated summaries. Using the verb semantic relation, namely happens-before provided by VerbOcean, we construct an event term temporal relation graph for source documents. We assume that the maximal weakly connected component on this graph represents the main topic of source documents. The event terms in the temporal critical chain identified from the maximal weakly connected component are then used to calculate the significance of the sentences in source documents. The most significant sentences are included in final summaries. Experiments conducted on the DUC 2001 corpus show that extractive summarization based on event term temporal relation graph and critical chain is able to organize final summaries in a more coherent way and accordingly achieves encouraging improvement over the well-known tf*idf-based and PageRank-based approaches.