Please use this identifier to cite or link to this item:
|Title:||Combining coherence models and machine translation evaluation metrics for summarization evaluation|
|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.|
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 27, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.