One-Dimensional Object Detection for Streaming Text Segmentation of Meeting Dialogue (2025)

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.

## introduction with the advancement and widespread adoption of automatic speech recognition (asr) technology, along with the increasing societal demand for realtime recording and processing of dialogue content, streaming text segmentation (sts) has become a key task in real-time dialogue preprocessing and has gained increasing attention. in scenarios such as business meetings, academic seminars, consulting services, training, and speeches, lengthy dialogue content frequently occurs, and handling this massive streaming information poses a challenge. effectively segmenting dialogue content into coherent thematic paragraphs @xcite greatly facilitates subsequent understanding and analysis, such as in dialogue summarization @xcite @xcite , question answering @xcite , and information retrieval @xcite . although text segmentation technology has made significant progress, research on sts is still relatively scarce. while some existing segmentation techniques can be applied to sts, how to further improve the accuracy of streaming segmentation, optimize processing workflows, and reduce latency remains an urgent issue to explore. segmenting based on sentence granularity using text transcribed by asr is often impractical because there may not be sufficient context to determine whether the current utterance is a segmentation boundary. to address this issue, we adopted self-adaptive sliding window @xcite , performing segmentation at the window level on streaming text. supervised text segmentation tasks are commonly interpreted as sentence classification tasks, among these, the issue of imbalance is particularly pronounced, especially within long texts, resulting in sparse segmentation labels that impair model performance and make the training process slow and unstable. furthermore, interpreting text segmentation tasks merely as sentence classification tasks is insufficient because this perspective overlooks paragraph content, paragraph length @xcite , and the intrinsic structure of the text, considering only boundary locations; more importantly, there is a significant disparity between text segmentation and sentence classification tasks. in order to solve the existing problems and improve the accuracy of sts, our research and contributions can be summarized as follows: the results demonstrate that the 1dod model possesses excellent generalization capability and universality. ## experimental setup specific parameter settings can be found in the parameter setting. ## experimental results and analysis in this section, we first present the test results on amc and the transfer test results on mdt1553, then analyze the ablation experiments, and finally demonstrate the stability of our method in various aspects. ## conclusion in text segmentation tasks, the imbalance of labels affects the stability and convergence of training, and the discrepancy between task objectives and training objectives impacts model performance. in response, we proposed balanced labels and a 1dod task under the sts framework to address these two pain points. experimental results show that our method far surpasses previous text segmentation models and possesses strong generalization capabilities and stability. in the future, we plan to extend this work to segmentation applications across various types of streaming media.