  Meeting summarization is a challenging task due to its dynamic interaction
nature among multiple speakers and lack of sufficient training data. Existing
methods view the meeting as a linear sequence of utterances while ignoring the
diverse relations between each utterance. Besides, the limited labeled data
further hinders the ability of data-hungry neural models. In this paper, we try
to mitigate the above challenges by introducing dialogue-discourse relations.
First, we present a Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to
explicitly model the interaction between utterances in a meeting by modeling
different discourse relations. The core module is a relational graph encoder,
where the utterances and discourse relations are modeled in a graph interaction
manner. Moreover, we devise a Dialogue Discourse-Aware Data Augmentation
(DDADA) strategy to construct a pseudo-summarization corpus from existing input
meetings, which is 20 times larger than the original dataset and can be used to
pretrain DDAMS. Experimental results on AMI and ICSI meeting datasets show that
our full system can achieve SOTA performance. Our codes will be available at:
https://github.com/xcfcode/DDAMS.
