Abstract
When writing a summary, humans tend to
choose content from one or two sentences and
merge them into a single summary sentence.
However, the mechanisms behind the selection of one or multiple source sentences remain
poorly understood. Sentence fusion assumes
multi-sentence input; yet sentence selection
methods only work with single sentences and
not combinations of them. There is thus a crucial gap between sentence selection and fusion
to support summarizing by both compressing
single sentences and fusing pairs. This paper attempts to bridge the gap by ranking sentence singletons and pairs together in a uni-
fied space. Our proposed framework attempts
to model human methodology by selecting either a single sentence or a pair of sentences,
then compressing or fusing the sentence(s) to
produce a summary sentence. We conduct extensive experiments on both single- and multidocument summarization datasets and report
findings on sentence selection and abstraction