Entity-based De-noising Modeling for Controllable Dialogue Summarization

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Entity-based De-noising Modeling for Controllable Dialogue Summarization
Title:
Entity-based De-noising Modeling for Controllable Dialogue Summarization
Other Titles:
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
DOI:
Keywords:
Publication Date:
25 September 2022
Citation:
Zhengyuan Liu and Nancy Chen. 2022. Entity-based De-noising Modeling for Controllable Dialogue Summarization. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 407–418, Edinburgh, UK. Association for Computational Linguistics.
Abstract:
Although fine-tuning pre-trained backbones produces fluent and grammatically-correct text in various language generation tasks, factual consistency in abstractive summarization remains challenging. This challenge is especially thorny for dialogue summarization, where neural models often make inaccurate associations between personal named entities and their respective actions. To tackle this type of hallucination, we present an entity-based de-noising model via text perturbation on reference summaries. We then apply this proposed approach in beam search validation, conditional training augmentation, and inference post-editing. Experimental results on the SAMSum corpus show that state-of-the-art models equipped with our proposed method achieve generation quality improvement in both automatic evaluation and human assessment.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research is supported by core funding from: Institute for Infocomm Research (I2R)
Grant Reference no. : N/A

This research / project is supported by the National Research Foundation, Prime Minister’s Office, - Campus for Research Excellence and Technological Enterprise (CREATE)
Grant Reference no. : N.A
Description:
ISBN:
2022.sigdial-1.40
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