  Recent approaches to English-language sentence compression rely on parallel
corpora consisting of sentence-compression pairs. However, a sentence may be
shortened in many different ways, which each might be suited to the needs of a
particular application. Therefore, in this work, we collect and model
crowdsourced judgements of the acceptability of many possible sentence
shortenings. We then show how a model of such judgements can be used to support
a flexible approach to the compression task. We release our model and dataset
for future work.
