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|dc.title||Combining coherence models and machine translation evaluation metrics for summarization evaluation|
|dc.identifier.citation||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.|
|dc.description.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.|
|dc.description.sourcetitle||50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference|
|Appears in Collections:||Staff Publications|
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