Daniil Parinov


2024

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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
Findings of the Association for Computational Linguistics: NAACL 2024

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.