NAACL 2024findings2 citations
Leveraging Summarization for Unsupervised Dialogue Topic Segmentation
Aleksei Artemiev, Daniil Parinov, Alexey Grishanov, Ivan Borisov, Alexey Vasilev, Daniil Muravetskii, Aleksey Rezvykh, Aleksei Goncharov
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.
BibTeX
@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/",
doi = "10.18653/v1/2024.findings-naacl.291",
pages = "4697--4704"
}