Leveraging Summarization for Unsupervised Dialogue Topic Segmentation

Aleksei Artemiev, Daniil Parinov, Alexey Grishanov, Ivan Borisov, Alexey Vasilev, Daniil Muravetskii, Aleksey Rezvykh, Aleksei Goncharov, Andrey Savchenko


Abstract
Traditional approaches to dialogue segmentation perform reasonably well on synthetic or written dialogues but suffer when dealing with spoken, noisy dialogs. In addition, such methods require careful tuning of hyperparameters. We propose to leverage a novel approach that is based on dialogue summaries. Experiments on different datasets showed that the new approach outperforms popular state-of-the-art algorithms in unsupervised topic segmentation and requires less setup.
Anthology ID:
2024.findings-naacl.291
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
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Publisher:
Association for Computational Linguistics
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Pages:
4697–4704
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URL:
https://aclanthology.org/2024.findings-naacl.291
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Cite (ACL):
Aleksei Artemiev, Daniil Parinov, Alexey Grishanov, Ivan Borisov, Alexey Vasilev, Daniil Muravetskii, Aleksey Rezvykh, Aleksei Goncharov, and Andrey Savchenko. 2024. Leveraging Summarization for Unsupervised Dialogue Topic Segmentation. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 4697–4704, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Leveraging Summarization for Unsupervised Dialogue Topic Segmentation (Artemiev et al., Findings 2024)
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https://aclanthology.org/2024.findings-naacl.291.pdf
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