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

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

12 Jun 2019ACL 2019 7arXiv:1906.05012archive 2025-07-28

Kai Wang, Xiaojun Quan, Rui Wang

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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