论文标题
通过异质图网络将常识性知识纳入抽象性对话摘要
Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks
论文作者
论文摘要
抽象性对话摘要是捕获对话的亮点并将其重写为简洁版本的任务。在本文中，我们提出了一个新颖的多演讲者对话摘要，以证明大规模常识知识如何促进对话理解和摘要产生。详细说明，我们将话语和常识性知识视为两种不同类型的数据，并设计了对话异质图网络（D-HGN），用于建模这两个信息。同时，我们还将扬声器添加为异质节点，以促进信息流。 Samsum数据集的实验结果表明，我们的模型可以胜过各种方法。我们还对论证对话摘要语料库进行了零射击设置实验，结果表明，我们的模型可以更好地推广到新领域。
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.
