@inproceedings{he-etal-2025-one,
title = "One-Dimensional Object Detection for Streaming Text Segmentation of Meeting Dialogue",
author = "He, Rui and
Wang, Zhongqing and
Qiang, Minjie and
Wang, Hongling and
Yifan.zhang, Yifan.zhang and
Xu, Hua and
Fan, Shuai and
Zhou, Guodong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.213/",
doi = "10.18653/v1/2025.findings-acl.213",
pages = "4118--4130",
ISBN = "979-8-89176-256-5",
abstract = "Dialogue text segmentation aims to partition dialogue content into consecutive paragraphs based on themes or logic, enhancing its comprehensibility and manageability. Current text segmentation models, when applied directly to STS (Streaming Text Segmentation), exhibit numerous limitations, such as imbalances in labels that affect the stability of model training, and discrepancies between the model{'}s training tasks (sentence classification) and the actual text segmentation that limit the model{'}s segmentation capabilities.To address these challenges, we first implement STS for the first time using a sliding window-based segmentation method. Secondly, we employ two different levels of sliding window-based balanced label strategies to stabilize the training process of the streaming segmentation model and enhance training convergence speed. Finally, by adding a one-dimensional bounding-box regression task for text sequences within the window, we restructure the training approach of STS tasks, shifting from sentence classification to sequence segmentation, thereby aligning the training objectives with the task objectives, which further enhanced the model{'}s performance. Extensive experimental results demonstrate that our method is robust, controllable, and achieves state-of-the-art performance."
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<abstract>Dialogue text segmentation aims to partition dialogue content into consecutive paragraphs based on themes or logic, enhancing its comprehensibility and manageability. Current text segmentation models, when applied directly to STS (Streaming Text Segmentation), exhibit numerous limitations, such as imbalances in labels that affect the stability of model training, and discrepancies between the model’s training tasks (sentence classification) and the actual text segmentation that limit the model’s segmentation capabilities.To address these challenges, we first implement STS for the first time using a sliding window-based segmentation method. Secondly, we employ two different levels of sliding window-based balanced label strategies to stabilize the training process of the streaming segmentation model and enhance training convergence speed. Finally, by adding a one-dimensional bounding-box regression task for text sequences within the window, we restructure the training approach of STS tasks, shifting from sentence classification to sequence segmentation, thereby aligning the training objectives with the task objectives, which further enhanced the model’s performance. Extensive experimental results demonstrate that our method is robust, controllable, and achieves state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T One-Dimensional Object Detection for Streaming Text Segmentation of Meeting Dialogue
%A He, Rui
%A Wang, Zhongqing
%A Qiang, Minjie
%A Wang, Hongling
%A Yifan.zhang, Yifan.zhang
%A Xu, Hua
%A Fan, Shuai
%A Zhou, Guodong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F he-etal-2025-one
%X Dialogue text segmentation aims to partition dialogue content into consecutive paragraphs based on themes or logic, enhancing its comprehensibility and manageability. Current text segmentation models, when applied directly to STS (Streaming Text Segmentation), exhibit numerous limitations, such as imbalances in labels that affect the stability of model training, and discrepancies between the model’s training tasks (sentence classification) and the actual text segmentation that limit the model’s segmentation capabilities.To address these challenges, we first implement STS for the first time using a sliding window-based segmentation method. Secondly, we employ two different levels of sliding window-based balanced label strategies to stabilize the training process of the streaming segmentation model and enhance training convergence speed. Finally, by adding a one-dimensional bounding-box regression task for text sequences within the window, we restructure the training approach of STS tasks, shifting from sentence classification to sequence segmentation, thereby aligning the training objectives with the task objectives, which further enhanced the model’s performance. Extensive experimental results demonstrate that our method is robust, controllable, and achieves state-of-the-art performance.
%R 10.18653/v1/2025.findings-acl.213
%U https://aclanthology.org/2025.findings-acl.213/
%U https://doi.org/10.18653/v1/2025.findings-acl.213
%P 4118-4130
Markdown (Informal)
[One-Dimensional Object Detection for Streaming Text Segmentation of Meeting Dialogue](https://aclanthology.org/2025.findings-acl.213/) (He et al., Findings 2025)
ACL
- Rui He, Zhongqing Wang, Minjie Qiang, Hongling Wang, Yifan.zhang Yifan.zhang, Hua Xu, Shuai Fan, and Guodong Zhou. 2025. One-Dimensional Object Detection for Streaming Text Segmentation of Meeting Dialogue. In Findings of the Association for Computational Linguistics: ACL 2025, pages 4118–4130, Vienna, Austria. Association for Computational Linguistics.