@inproceedings{artemiev-etal-2024-leveraging,
title = "Leveraging Summarization for Unsupervised Dialogue Topic Segmentation",
author = "Artemiev, Aleksei and
Parinov, Daniil and
Grishanov, Alexey and
Borisov, Ivan and
Vasilev, Alexey and
Muravetskii, Daniil and
Rezvykh, Aleksey and
Goncharov, Aleksei and
Savchenko, Andrey",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.291",
pages = "4697--4704",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Leveraging Summarization for Unsupervised Dialogue Topic Segmentation
%A Artemiev, Aleksei
%A Parinov, Daniil
%A Grishanov, Alexey
%A Borisov, Ivan
%A Vasilev, Alexey
%A Muravetskii, Daniil
%A Rezvykh, Aleksey
%A Goncharov, Aleksei
%A Savchenko, Andrey
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F artemiev-etal-2024-leveraging
%X 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.
%U https://aclanthology.org/2024.findings-naacl.291
%P 4697-4704
Markdown (Informal)
[Leveraging Summarization for Unsupervised Dialogue Topic Segmentation](https://aclanthology.org/2024.findings-naacl.291) (Artemiev et al., Findings 2024)
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.