Improving Factual Consistency of Dialogue Summarization with Fact-Augmentation Mechanism

No Thumbnail Available
File version
Author(s)
Li, W
Zhou, X
Bai, X
Pan, S
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2022
Size
File type(s)
Location

Padua, Italy

License
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.

Journal Title
Conference Title

2022 International Joint Conference on Neural Networks (IJCNN)

Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject

Neural networks

Persistent link to this record
Citation

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