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.

本研究重新审视单词聚类方法，通过将子句命题分组，从而能更精确地对齐信息。我们的方法检测重要的命题，将它们聚类形成释义的群集，并通过文本融合为每个聚类生成代表性句子。在 DUC2004 和 TAC2011 数据集中，我们的总结方法在自动 ROUGE 分数和人类偏好方面优于先前的 MDS 方法。