Abstract
Topic segmentation aims to detect topic boundaries and split automatic speech recognition transcriptions (e.g., meeting transcripts) into segments that are bounded by thematic meanings. In this work, we propose M3Seg, a novel Maximum-Minimum Mutual information paradigm for linear topic segmentation without using any parallel data. Specifically, by employing sentence representations provided by pre-trained language models, M3Seg first learns a region-based segment encoder based on the maximization of mutual information between the global segment representation and the local contextual sentence representation. Secondly, an edge-based boundary detection module aims to segment the whole by topics based on minimizing the mutual information between different segments. Experiment results on two public datasets demonstrate the effectiveness of M3Seg, which outperform the state-of-the-art methods by a significant (18%–37% improvement) margin.- Anthology ID:
- 2023.emnlp-main.492
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7928–7934
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.492
- DOI:
- 10.18653/v1/2023.emnlp-main.492
- Cite (ACL):
- Ke Wang, Xiutian Zhao, Yanghui Li, and Wei Peng. 2023. M3Seg: A Maximum-Minimum Mutual Information Paradigm for Unsupervised Topic Segmentation in ASR Transcripts. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7928–7934, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- M3Seg: A Maximum-Minimum Mutual Information Paradigm for Unsupervised Topic Segmentation in ASR Transcripts (Wang et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.emnlp-main.492.pdf