Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization
This work addresses the problem of generating coherent and factually accurate summaries from dialogues, which is incremental with novel modules for a specific domain.
The authors tackled dialogue summarization by proposing a model with supporting utterance flow modeling and fact regularization, achieving improved coherence and factual correctness as demonstrated on existing and new datasets.
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.