Traditional approaches to extractive summarization rely heavily on humanengineered 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 1 . 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.
Neural Summarization by Extracting Sentences and Words
Published 2016 in Annual Meeting of the Association for Computational Linguistics
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- Publication year
2016
- Venue
Annual Meeting of the Association for Computational Linguistics
- Publication date
2016-03-23
- Fields of study
Computer Science
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