  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 unified 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 multi-document summarization datasets and
report findings on sentence selection and abstraction.
