Improving Factual Consistency of Dialogue Summarization with Fact-Augmentation Mechanism

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Li, W
Zhou, X
Bai, X
Pan, S
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2022
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Abstract

With the vigorous development of dialogue system in natural language processing fields, dialogue summarization has attracted the attention of more scholars, which aims to extract brief introduction and hit points from dialogue information for readers. As the participation of multiple roles and the trans-formation of perspectives in dialogue, one of the most difficult problems, i.e. factual consistency, is raised in the generation of dialogue summaries. It means that the summaries are usually factual wrong although with high matching metric by many methods. Previous methods improve factual consistency between source and target by incorporating knowledge. However, the role of knowledge for dialogue summarization models lacks convincing evidence. In this paper, we propose a Fact-Augmentation (FA) Mechanism based Dialogue Summarization model, called FA-DS model for dialogue summarization. Our FA-DS integrates the fact graph extracted from the dialogue into the summaries generation process, and augment the gain of the factual information through the fact-aware score penalty item. Experiments on the large-scale dialogue dataset SAMSum demonstrate that our fact-augmentation mechanism can improve the quality and factual consistency of dialogue summarization.

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2022 International Joint Conference on Neural Networks (IJCNN)
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Neural networks
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Li, W; Zhou, X; Bai, X; Pan, S, Improving Factual Consistency of Dialogue Summarization with Fact-Augmentation Mechanism, 2022 International Joint Conference on Neural Networks (IJCNN), 2022