Before revision:  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.
After revision:  In this paper, we develop a Dialogue Discourse-Aware Data Augmentation (DDADA) strategy 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.