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BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization

2019-06-12ACL 2019Code Available0· sign in to hype

Kai Wang, Xiaojun Quan, Rui Wang

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Abstract

The success of neural summarization models stems from the meticulous encodings of source articles. To overcome the impediments of limited and sometimes noisy training data, one promising direction is to make better use of the available training data by applying filters during summarization. In this paper, we propose a novel Bi-directional Selective Encoding with Template (BiSET) model, which leverages template discovered from training data to softly select key information from each source article to guide its summarization process. Extensive experiments on a standard summarization dataset were conducted and the results show that the template-equipped BiSET model manages to improve the summarization performance significantly with a new state of the art.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
GigaWordBiSETROUGE-139.11Unverified

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