Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints
Greg Durrett, Taylor Berg-Kirkpatrick, Dan Klein · 2016
We present a discriminative model for single-document summarization that integrally combines compression and anaphoricity constraints.Our model selects textual units to include in the summary based on a rich set of sparse features whose weights are learned on a large corpus.We allow for the deletion of content within a sentence when that deletion is licensed by compression rules; in our framework, these are implemented as dependencies between subsentential units of text.Anaphoricity constraints then improve cross-sentence coherence by guaranteeing that, for each pronoun included in the summary, the pronoun's antecedent is included as well or the pronoun is rewritten as a full mention.When trained end-to-end, our final system 1 outperforms prior work on both ROUGE as well as on human judgments of linguistic quality.