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arXiv · 1603.08887

Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints

Greg Durrett·Taylor Berg-Kirkpatrick·Dan Klein
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Abstract

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 outperforms prior work on both ROUGE as well as on human judgments of linguistic quality.

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Greg Durrett, Taylor Berg-Kirkpatrick, Dan Klein. 2016-03-29. Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints. https://arxiv.org/abs/1603.08887

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