Dialogue summarization aims to generate a summary that indicates the key
points of a given dialogue. In this work, we propose an end-to-end neural model
for dialogue summarization with two novel modules, namely, the \emph{supporting
utterance flow modeling module} and the \emph{fact regularization module}. The
supporting utterance flow modeling helps to generate a coherent summary by
smoothly shifting the focus from the former utterances to the later ones. The
fact regularization encourages the generated summary to be factually consistent
with the ground-truth summary during model training, which helps to improve the
factual correctness of the generated summary in inference time. Furthermore, we
also introduce a new benchmark dataset for dialogue summarization. Extensive
experiments on both existing and newly-introduced datasets demonstrate the
effectiveness of our model.

本文提出了一种基于端到端神经模型的对话摘要生成方法，该方法包括支持话语流建模模块和事实规范化模块；支持话语流建模有助于生成连贯的摘要，而事实规范化则有利于提高生成摘要的事实正确性；同时引入了一个新的基准数据集用于对话摘要生成，实验表明该方法的有效性和高性能。