Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/78061
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dc.titleCombining coherence models and machine translation evaluation metrics for summarization evaluation
dc.contributor.authorLin, Z.
dc.contributor.authorLiu, C.
dc.contributor.authorNg, H.T.
dc.contributor.authorKan, M.-Y.
dc.date.accessioned2014-07-04T03:11:57Z
dc.date.available2014-07-04T03:11:57Z
dc.date.issued2012
dc.identifier.citationLin, Z.,Liu, C.,Ng, H.T.,Kan, M.-Y. (2012). Combining coherence models and machine translation evaluation metrics for summarization evaluation. 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference 1 : 1006-1014. ScholarBank@NUS Repository.
dc.identifier.isbn9781937284244
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/78061
dc.description.abstractAn ideal summarization system should produce summaries that have high content coverage and linguistic quality. Many state-ofthe- art summarization systems focus on content coverage by extracting content-dense sentences from source articles. A current research focus is to process these sentences so that they read fluently as a whole. The current AESOP task encourages research on evaluating summaries on content, readability, and overall responsiveness. In this work, we adapt a machine translation metric to measure content coverage, apply an enhanced discourse coherence model to evaluate summary readability, and combine both in a trained regression model to evaluate overall responsiveness. The results show significantly improved performance over AESOP 2011 submitted metrics. © 2012 Association for Computational Linguistics.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitle50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference
dc.description.volume1
dc.description.page1006-1014
dc.identifier.isiutNOT_IN_WOS
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