Human acceptability judgements for extractive sentence compression
This work addresses the need for adaptable sentence compression in NLP applications, though it is incremental as it builds on existing corpus-based methods.
The authors tackled the problem of sentence compression by collecting crowdsourced acceptability judgments for multiple possible shortenings, enabling a flexible approach to the task, and they released the model and dataset for future use.
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.