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2012

Combining Coherence Models and Machine Translation Evaluation Metrics for Summarization Evaluation

7 years 2 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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