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arXiv · 2109.06046

Augmented Abstractive Summarization With Document-LevelSemantic Graph

Qiwei Bi·Haoyuan Li·Kun Lu·Hanfang Yang
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Abstract

Previous abstractive methods apply sequence-to-sequence structures to generate summary without a module to assist the system to detect vital mentions and relationships within a document. To address this problem, we utilize semantic graph to boost the generation performance. Firstly, we extract important entities from each document and then establish a graph inspired by the idea of distant supervision \citep{mintz-etal-2009-distant}. Then, we combine a Bi-LSTM with a graph encoder to obtain the representation of each graph node. A novel neural decoder is presented to leverage the information of such entity graphs. Automatic and human evaluations show the effectiveness of our technique.

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Qiwei Bi, Haoyuan Li, Kun Lu, Hanfang Yang. 2021-09-13. Augmented Abstractive Summarization With Document-LevelSemantic Graph. https://doi.org/10.6339/21-jds1012

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