  Abstractive dialogue summarization is the task of capturing the highlights of
a dialogue and rewriting them into a concise version. In this paper, we present
a novel multi-speaker dialogue summarizer to demonstrate how large-scale
commonsense knowledge can facilitate dialogue understanding and summary
generation. In detail, we consider utterance and commonsense knowledge as two
different types of data and design a Dialogue Heterogeneous Graph Network
(D-HGN) for modeling both information. Meanwhile, we also add speakers as
heterogeneous nodes to facilitate information flow. Experimental results on the
SAMSum dataset show that our model can outperform various methods. We also
conduct zero-shot setting experiments on the Argumentative Dialogue Summary
Corpus, the results show that our model can better generalized to the new
domain.
