Dialogue Discourse-Aware Graph Convolutional Networks for Abstractive Meeting Summarization

• URL: http://arxiv.org/abs/2012.03502v1
• Date: Mon, 7 Dec 2020 07:51:38 GMT
• Title: Dialogue Discourse-Aware Graph Convolutional Networks for Abstractive Meeting Summarization
• Authors: Xiachong Feng, Xiaocheng Feng, Bing Qin, Xinwei Geng, Ting Liu
• Abstract summary: We develop a dialogue discourse-Aware Graph Convolutional Networks (DDA-GCN) for meeting summarization. We first transform the entire meeting text with dialogue discourse relations into a discourse graph and then use DDA-GCN to encode the semantic representation of the graph. Finally, we employ a Recurrent Neural Network to generate the summary.
• Score: 24.646506847760822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequence-to-sequence methods have achieved promising results for textual abstractive meeting summarization. Different from documents like news and scientific papers, a meeting is naturally full of dialogue-specific structural information. However, previous works model a meeting in a sequential manner, while ignoring the rich structural information. In this paper, we develop a Dialogue Discourse-Aware Graph Convolutional Networks (DDA-GCN) for meeting summarization by utilizing dialogue discourse, which is a dialogue-specific structure that can provide pre-defined semantic relationships between each utterance. We first transform the entire meeting text with dialogue discourse relations into a discourse graph and then use DDA-GCN to encode the semantic representation of the graph. Finally, we employ a Recurrent Neural Network to generate the summary. In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus, which can be used to pre-train our model. Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.

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