Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks

Understand

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.