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Traditional approaches to extractive summarization rely heavily on human engineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features. We develop a general framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor. This architecture allows us to develop different classes of summarization models which can extract sentences or words. We train our models on large scale corpora containing hundreds of thousands of document-summary pairs. Experimental results on two summarization datasets demonstrate that our models obtain results comparable to the state of the art without any access to linguistic annotation.
|Title of host publication||Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics|
|Place of Publication||Berlin, Germany|
|Publisher||Association for Computational Linguistics|
|Number of pages||11|
|Publication status||Published - 12 Aug 2016|
|Event||54th Annual Meeting of the Association for Computational Linguistics - Berlin, Germany|
Duration: 7 Aug 2016 → 12 Aug 2016
|Conference||54th Annual Meeting of the Association for Computational Linguistics|
|Abbreviated title||ACL 2016|
|Period||7/08/16 → 12/08/16|
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1/09/16 → 31/08/22