Unsupervised Sentence Enhancement for Automatic Summarization

We present sentence enhancement as a novel technique for text-to-text genera-tion in abstractive summarization. Com-pared to extraction or previous approaches to sentence fusion, sentence enhancement increases the range of possible summary sentences by allowing the combination of dependency subtrees from any sentence from the source text. Our experiments in-dicate that our approach yields summary sentences that are competitive with a sen-tence fusion baseline in terms of con-tent quality, but better in terms of gram-maticality, and that the benefit of sen-tence enhancement relies crucially on an event coreference resolution algorithm us-ing distributional semantics. We also consider how text-to-text generation ap-proaches to summarization can be ex-tended beyond the source text by exam-ining how human summary writers incor-porate source-text-external elements into their summary sentences. 1