  Multi Sentence Compression (MSC) is of great value to many real world
applications, such as guided microblog summarization, opinion summarization and
newswire summarization. Recently, word graph-based approaches have been
proposed and become popular in MSC. Their key assumption is that redundancy
among a set of related sentences provides a reliable way to generate
informative and grammatical sentences. In this paper, we propose an effective
approach to enhance the word graph-based MSC and tackle the issue that most of
the state-of-the-art MSC approaches are confronted with: i.e., improving both
informativity and grammaticality at the same time. Our approach consists of
three main components: (1) a merging method based on Multiword Expressions
(MWE); (2) a mapping strategy based on synonymy between words; (3) a re-ranking
step to identify the best compression candidates generated using a POS-based
language model (POS-LM). We demonstrate the effectiveness of this novel
approach using a dataset made of clusters of English newswire sentences. The
observed improvements on informativity and grammaticality of the generated
compressions show that our approach is superior to state-of-the-art MSC
methods.
