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.
CITATION STYLE
Feng, X., Feng, X., & Qin, B. (2021). Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12869 LNAI, pp. 127–142). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-84186-7_9
Mendeley helps you to discover research relevant for your work.