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Multi Sentence Compression (MSC) is of great value to many real worldapplications, such as guided microblog summarization, opinion summarization andnewswire summarization. Recently, word graph-based approaches have beenproposed and become popular in MSC. Their key assumption is that redundancyamong a set of related sentences provides a reliable way to generateinformative and grammatical sentences. In this paper, we propose an effectiveapproach to enhance the word graph-based MSC and tackle the issue that most ofthe state-of-the-art MSC approaches are confronted with: i.e., improving bothinformativity and grammaticality at the same time. Our approach consists ofthree main components: (1) a merging method based on Multiword Expressions(MWE); (2) a mapping strategy based on synonymy between words; (3) a re-rankingstep to identify the best compression candidates generated using a POS-basedlanguage model (POS-LM). We demonstrate the effectiveness of this novelapproach using a dataset made of clusters of English newswire sentences. Theobserved improvements on informativity and grammaticality of the generatedcompressions show that our approach is superior to state-of-the-art MSCmethods.

On Improving Informativity and Grammaticality for Multi-Sentence Compression