  Text clustering methods were traditionally incorporated into multi-document
summarization (MDS) as a means for coping with considerable information
repetition. Particularly, clusters were leveraged to indicate information
saliency as well as to avoid redundancy. Such prior methods focused on
clustering sentences, even though closely related sentences usually contain
also non-aligned parts. In this work, we revisit the clustering approach,
grouping together sub-sentential propositions, aiming at more precise
information alignment. Specifically, our method detects salient propositions,
clusters them into paraphrastic clusters, and generates a representative
sentence for each cluster via text fusion. Our summarization method improves
over the previous state-of-the-art MDS method in the DUC 2004 and TAC 2011
datasets, both in automatic ROUGE scores and human preference.
