Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/78061
Title: Combining coherence models and machine translation evaluation metrics for summarization evaluation
Authors: Lin, Z.
Liu, C. 
Ng, H.T. 
Kan, M.-Y. 
Issue Date: 2012
Source: Lin, 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.
Abstract: An 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.
Source Title: 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/78061
ISBN: 9781937284244
Appears in Collections:Staff Publications

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