Leveraging Summarization for Unsupervised Dialogue Topic Segmentation (2024.findings-naacl)
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Aleksei Artemiev, Daniil Parinov, Alexey Grishanov, Ivan Borisov, Alexey Vasilev, Daniil Muravetskii, Aleksey Rezvykh, Aleksei Goncharov, Andrey Savchenko
| Challenge: | Existing methods to segment textual data are difficult to handle for noisy spoken dialogues. |
| Approach: | They propose to leverage dialogue summaries for unsupervised topic segmentation . they show that the new approach outperforms state-of-the-art methods in unsupervised segmentation and requires less setup . |
| Outcome: | The proposed approach outperforms state-of-the-art methods in unsupervised topic segmentation and requires less setup. |
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