@inproceedings{wang-etal-2023-m3seg,
    title = "{M}$^3${S}eg: A Maximum-Minimum Mutual Information Paradigm for Unsupervised Topic Segmentation in {ASR} Transcripts",
    author = "Wang, Ke  and
      Zhao, Xiutian  and
      Li, Yanghui  and
      Peng, Wei",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.emnlp-main.492/",
    doi = "10.18653/v1/2023.emnlp-main.492",
    pages = "7928--7934",
    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 M$^3$Seg, 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, M$^3$Seg 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 M$^3$Seg, which outperform the state-of-the-art methods by a significant (18{\%}{--}37{\%} improvement) margin."
}