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.
@article{arxiv.1902.00489,
title = {Human acceptability judgements for extractive sentence compression},
author = {Abram Handler and Brian Dillon and Brendan O'Connor},
journal= {arXiv preprint arXiv:1902.00489},
year = {2019}
}