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ACL
2012

Combining Coherence Models and Machine Translation Evaluation Metrics for Summarization Evaluation

11 years 11 months ago
Combining Coherence Models and Machine Translation Evaluation Metrics for Summarization Evaluation
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.
Ziheng Lin, Chang Liu, Hwee Tou Ng, Min-Yen Kan
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where ACL
Authors Ziheng Lin, Chang Liu, Hwee Tou Ng, Min-Yen Kan
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