One-Dimensional Object Detection for Streaming Text Segmentation of Meeting Dialogue
Authors: Rui He, Zhongqing Wang, Minjie Qiang, Hongling Wang, Yifan.zhang, Hua Xu, Shuai Fan, Guodong Zhou
Accepted at: acl25

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

Rui He 1,2, Zhongqing Wang 1, Minjie Qiang 1, Hongling Wang 1*
Yifan Zhang 2, Hua Xu 2, Shuai Fan 2, Guodong Zhou 1
1 Natural Language Processing Lab, Soochow University, Suzhou, China
2 AISpeech Ltd., Suzhou, China
rhesudanlp@stu.suda.edu.cn

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.

1 Introduction

With the advancement and widespread adoption of Automatic Speech Recognition (ASR) technology, along with the increasing societal demand for real-time 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 (Brants et al., 2002) greatly facilitates subsequent understanding and analysis, such as in dialogue summarization (Qi et al., 2021; Schneider and Turchi, 2023; Li et al., 2019), question answering (Yoon et al., 2018), and information retrieval (Zhu et al., 2019).

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

![The dialogue content is segmented in real-time into paragraphs, with each paragraph representing a semantically complete conversation that implicitly contains a specific topic.](page_1047_362_355_246.png)

Figure 1: The dialogue content is segmented in real-time into paragraphs, with each paragraph representing a semantically complete conversation that implicitly contains a specific topic.
self-adaptive sliding window (Zhang et al., 2021), 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 (Yoo and Kim, 2024), 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:

• To our knowledge, our method is the first to address the STS problem and resolve the issue of imbalanced training labels in text segmentation. First, we achieve STS by sampling and segmenting dialogue texts using a sliding window. Second, we introduce two levels of balanced labels to replace conventional text segmentation labels, enabling the model to converge faster and more stably during training, while effectively controlling paragraph lengths during inference.

• Inspired by object detection and semantic segmentation in images, we implemented a one-dimensional boundary-box regression task for text sequences and applied it to window-based STS, addressing the mismatch between task objectives and training objectives in text segmentation, thereby optimizing task objectives more directly. We propose the One-Dimensional Object Detection (1DOD) method, which jointly optimizes the model with a 1D boundary-box regression task and a sentence classification task, to enhance the performance of the text segmentation model.

• Our method has shown improvements over the SOTA models on the AMC dataset by 11.87 and 10.86 on the \( A \) and \( B \) respectively, and has reduced the traditional text segmentation evaluation metrics \( WD \) and \( P_k \) by 4.19 and 2.17 respectively. Furthermore, we have contributed a large-scale and extensive dataset, which has been used to validate our method. The results demonstrate that the 1DOD model possesses excellent generalization capability and universality.

2 Related Work

2.1 Text Segmentation

The popularity of deep neural networks has greatly propelled the development of the text segmentation field. Koshorek et al. (2018) and Wang et al. (2018) used Bi-LSTM to predict segment boundaries; SECTOR (Arnold et al., 2019) employs LSTM to predict sentence topics and merges them into paragraphs; Barrow et al. (2020) proposed S-LSTM for text segmentation; Xing et al. (2020) added a coherence auxiliary task to enhance the segmentation performance of the hierarchical attention BiLSTM; Yoo and Kim (2024) incorporated segment distance information in the model to control the paragraph segmentation length in novel texts; Glavas and Somasundaran (2020), Lo et al. (2021), and Lukasik et al. (2020) use hierarchical Transformer networks to extract sentence features for segmentation; Yu et al. (2023) proposed Topic-Aware Sentence Structure Prediction (TSSP) and Contrastive Semantic Similarity Learning (CSSP) to enhance topic-oriented text.

2.2 Dialogue Segmentation

Zhang and Zhou (2019) noted that speaker information helps with dialogue segmentation; Xing and Carenini (2021) and Solbiati et al. (2021) used an unsupervised Bert model to determine the thematic relevance or coherence of utterances; Zhang et al. (2021) utilized a self-adaptive sliding window for efficient text segmentation; Xu et al. (2021) proposed an unsupervised topic-aware segmentation algorithm and a Topic-Aware Dual Attention Matching (TADAM) network for dialogue segmentation; Xia et al. (2022) introduced the Neighbor Smoothing Parallel Extraction Network (PEN-NS) for dealing with segment boundary noise and ambiguity; (Gao et al., 2023) utilized neighboring utterance matching and pseudo-segmentation to enable the model to learn topic-aware utterance representations from unlabeled dialogue data.

Most existing works focus on incorporating additional topic information to aid segmentation, often neglecting the intrinsic characteristics of the text segmentation itself. In contrast, our approach does not introduce additional topic information but in-
Figure 2: In each window of STS, the training objective is to minimize classification loss and 1D bounding-box regression loss. The labels used in the figure are at the token-level. It should be noted that our 1D bounding-box regression task is single-edged.

stead focuses on enhancing the model’s semantic understanding for the STS task.

3 Method

3.1 Problem Formulation

In text segmentation tasks, given a dialogue text \( T = [s_1, s_2, \ldots, s_n] \), we segment it into consecutive paragraphs \( T = [P_1, P_2, \ldots, P_m] = [[s_1, \ldots, s_{p_1}], [s_{p_1+1}, \ldots, s_{p_2}], \ldots, [s_{p_{m-1}+1}, \ldots, s_n]] \), where \( s_{p_i}, i \in [1, \ldots, m] \) is the last sentence of \( P_i \). In the STS based on a self-adaptive sliding window, with a window size of \( x \), for the \( j \)-th window text \( T_{win_j} = [s_j, s_{j+1}, \ldots, s_{j+x-1}] \), the first paragraph of length \( l \) is segmented out as \( T_{para_j} = [s_j, s_{j+1}, \ldots, s_{j+l-1}] \), with the remaining as background paragraphs, where \( s_j \) is the first sentence of the background paragraph from the previous window, and \( s_{j+l} \) is the start of the next window.

3.2 Streaming Text Segmentation with Balanced Labels

Traditional supervised text segmentation methods select the last sentence of each paragraph as a classification task, while the self-adaptive sliding window method only selects the end sentence of the first paragraph within each window range. However, these segmentation methods, when dealing with lengthy dialogue texts, result in extremely imbalanced sample labels. For example, when segmenting a conference dialogue text with a thousand utterances into ten paragraphs, the direct segmentation ratio of positive to negative samples will reach 1:99; if the window size is set to 200, the ratio in each window will reach 1:199.

Lin et al. (2017) pointed out that extreme category imbalance can lead to inefficient training and model degradation. To address this issue, under the STS architecture, we propose two levels of balanced labels to replace the original task labels, based on sentence granularity and token granularity, as shown in Figure 3.

In segmentation tasks, for utterance granularity, we add the [EOP] token at the end of each sentence to indicate the end of a sentence. Typical text segmentation models classify each sentence’s [EOP] token to determine whether the sentence is the end of a paragraph. SeqModel (Zhang et al., 2021) uses the average pooled features of all tokens in the sentence for classification, with the same level of label sparsity as the former; in our proposed sentence-level segmentation task, within each window, the paragraph is determined by checking whether the [EOP] tokens of the first \( l \) sentences belong to the paragraph; similar to the sentence-level, the token-level adjusts by judging all tokens in the first \( l \) sentences, not just the [EOP] tokens.

<table>
  <tr>
    <th>General</th>
    <th>Sentence 1</th>
    <th>Sentence 2</th>
    <th>Sentence n</th>
  </tr>
  <tr>
    <td></td>
    <td>+100</td>
    <td>Neg</td>
    <td>-100</td>
    <td>Pos</td>
    <td>-100</td>
    <td>Neg</td>
  </tr>
  <tr>
    <th>Sentence level</th>
    <th>Sentence 1</th>
    <th>Sentence 2</th>
    <th>Sentence n</th>
  </tr>
  <tr>
    <td></td>
    <td>+100</td>
    <td>Pos</td>
    <td>-100</td>
    <td>Pos</td>
    <td>-100</td>
    <td>Neg</td>
  </tr>
  <tr>
    <th>Token level</th>
    <th>Sentence 1</th>
    <th>Sentence 2</th>
    <th>Sentence n</th>
  </tr>
  <tr>
    <td></td>
    <td>Pos</td>
    <td>Pos</td>
    <td>Pos</td>
    <td>Neg</td>
    <td>Neg</td>
    <td>Neg</td>
  </tr>
</table>

Figure 3: General text segmentation labels, sentence-level labels, and token-level labels. ‘Pos’ means it belongs to the target paragraph, while ‘Neg’ does not. ‘-100’ refers to ignoring this label.

By pre-adjusting to obtain the appropriate window size, the overall ratio of positive to negative labels in the training data can be made as close as possible to 1:1. Training with balanced labels allows the model to fully learn from both positive and negative labels, and enables the training process to
be quick and stable. Compared to sentence-level, token-level training labels provide more and richer balanced supervision signals, which we believe can enable the model to acquire more comprehensive information.

3.3 One-Dimensional Object Detection

In every step of STS, the task objective is to segment the first paragraph of the window text. Inspired by object detection and image semantic segmentation tasks, we treat the first paragraph as a 1D target within serialized text, and adapt the object detection task to one-dimension for application in STS.

DETR (Carion et al., 2020) is the first model to apply the Transformer to object detection, using a combined loss function of L1 and GIoU (Generalized Intersection over Union) (Rezatofighi et al., 2019) for bounding-box regression training, and additionally employs Dice (Milletari et al., 2016) loss for panoptic segmentation. For STS, we retain the cross-entropy loss for fine-grained category discrimination, which is essential as it effectively guides the model to capture and learn the semantic information of the text. We replace it with Focal (Lin et al., 2017) loss to better address imbalanced labels:

\[
\mathcal{L}_{cls} = - \frac{1}{N} \sum_{t=1}^{N} (\alpha(1-p_t)^{\gamma} y_t log p_t + (1-\alpha)p_t^{\gamma}(1-y_t)log(1-p_t))
\] (1)

Where, \( N \) is the number of samples, \( y_t \) is the true label of the \( t \)-th sample, \( p_t \) is the probability of predicting as the positive class, and \( \alpha \) and \( \gamma \) are the hyperparameters of Focal.

We apply the smooth L1 loss function to 1D bounding-box regression, primarily modifying the 2D bounding boxes to 1D. Since the left boundary of the paragraph always coincides with the left boundary of the window, the segmentation point coordinate is equivalent to the boundary width. For the active label \( B \) of length \( l_t \) and the segmentation paragraph prediction \( \hat{B} \) of length \( l_p \), their bounding boxes are \( (\frac{1}{2}l_t, l_t) \) and \( (\frac{1}{2}l_p, l_p) \) respectively, resulting in the smooth L1 loss function:

\[
\mathcal{L}_{Smooth\ L1} = \frac{1}{N} \sum_{t=1}^{N} \sum_{i \in \{c,l\}} smooth_{L1}(\hat{B}_i^t - B_i^t)
\] (2)

Where \( \hat{B}_i^t \) and \( B_i^t \) are the predicted and actual values of the \( i \)-th bounding box coordinate of the \( t \)-th sample respectively.

Then, we performed dimensionality reduction on the IoU (Yu et al., 2016) to one dimension and derived the loss function:

\[
\mathcal{L}_{IoU} = \frac{1}{N} \sum_{t=1}^{N} (1 - IoU_t)
\]
\[
= \frac{1}{N} \sum_{t=1}^{N} (1 - \frac{Intersection(B^t, \hat{B}^t)}{Union(B^t, \hat{B}^t)})
\] (3)

IoU has many variants, but most are not suitable for our 1D single-edge boundary regression, such as GIoU, CIoU (Zheng et al., 2020), and SIOU(Gevorgyan, 2022), because there is always an intersection when the predicted length is non-zero, and there is no aspect ratio or angle distinction. To accelerate training convergence, we have adapted the DIoU loss function to one dimension:

\[
\mathcal{R}_{DIoU} = \frac{\rho(B, \hat{B})}{Union(B, \hat{B})}
\] (4)

\[
\mathcal{L}_{DIoU} = \frac{1}{N} \sum_{t=1}^{N} (1 - DIoU_t)
\]
\[
= \frac{1}{N} \sum_{t=1}^{N} (1 - IoU_t + \mathcal{R}_{DIoU_t})
\] (5)

Where \( \rho(\cdot) \) represents the Euclidean distance, i.e., the distance between the midpoints of the two bounding boxes.

<table>
  <tr>
    <th>Target</th>
    <td>2</td>
    <td>5</td>
  </tr>
  <tr>
    <th>Prediction 1</th>
    <td>4</td>
    <td>3</td>
    <td>Iou=0.5, DIoU=0.25, ADIoU=0.32</td>
  </tr>
  <tr>
    <th>Prediction 2</th>
    <td>1</td>
    <td>6</td>
    <td>Iou=0.5, DIoU=0.25, ADIoU=0.50</td>
  </tr>
</table>

Figure 4: Comparison between the result of 1D IoU, DIoU and ADIoU.

However, both 1D IoU and DIoU do not address the issue of different scales having the same intersection over union ratio (Figure 4). Although cross-entropy loss and smooth L1 loss remain effective under such circumstances, we still explored solutions based on IoU. We simply calculated the average DIoU (ADIoU) between the segmented paragraph and the background paragraph as a metric and obtained the corresponding loss function:
ADIoU = \frac{1}{2}(DIOU + DIOU_{background}) \tag{6}

\mathcal{L}_{ADIoU} = \frac{1}{N} \sum_{t=1}^{N}(1 - ADIoU_t) \tag{7}

The 1D adaptation of the Dice loss function is similar to IoU; specifically, when using the ADIoU loss function, we use the corresponding average Dice (ADice):

\[
Dice = 2 \cdot \frac{Intersection(B, \hat{B})}{|B| + |\hat{B}|}
\] \tag{8}

ADice = \frac{1}{2}(Dice + Dice_{background}) \tag{9}

\mathcal{L}_{Dice} = \frac{1}{N} \sum_{t=1}^{N}(1 - Dice_t) \tag{10}

\mathcal{L}_{ADice} = \frac{1}{N} \sum_{t=1}^{N}(1 - ADice_t) \tag{11}

Overall, we obtain the total 1D bounding-box regression loss:

\[
\mathcal{L}_{1D\ bbox} = \lambda_{SL1}\mathcal{L}_{Smooth\ L1}
+ \begin{cases}
\lambda_{IoU}\mathcal{L}_{IoU} + \lambda_{Dice}\mathcal{L}_{Dice} \\
\lambda_{DIOU}\mathcal{L}_{DIOU} + \lambda_{Dice}\mathcal{L}_{Dice} \\
\lambda_{ADIoU}\mathcal{L}_{ADIoU} + \lambda_{ADice}\mathcal{L}_{ADice}
\end{cases}
\] \tag{12}

Where \( \lambda_{SL1}, \lambda_{IoU}, \lambda_{DIOU}, \lambda_{ADIoU}, \lambda_{Dice}, \) and \( \lambda_{ADice} \) are hyperparameters. Adding classification loss results in our final loss for 1DOD-based STS (Figure 2), which we apply in the training of the segmentation model:

\[
\mathcal{L}_{STS} = \mathcal{L}_{cls} + \mathcal{L}_{1D\ bbox}
\] \tag{13}

3.4 Tail Processing

When the window slides to the bottom, if the length of the background paragraph is less than the preset segmentation stop threshold \( t_{stop} \), the streaming segmentation stops; otherwise, the background paragraph continues to be segmented as the next window text. But obviously, the last part of the dialogue text is not detected. This leads to a problem: the tail paragraphs are prone to being segmented too short, and setting a larger threshold cannot prevent it; a larger threshold can also lead to excessively long tail paragraphs.

In response, we propose a new retrieval-merge process as a tail handling for STS: first, set an appropriate stop threshold \( t_{stop} \) to prevent overly long tail paragraphs; secondly, set a new minimum tail length threshold \( t_{min} \). After segmentation, if the tail paragraph is shorter than \( t_{min} \), a forward merging algorithm is executed. In forward merging, we use a pretrained model to determine whether the consecutive segments are part of a continuous piece or two independent paragraphs; if they are continuous, they are merged.

4 Experimental Setup

Specific parameter settings can be found in the Parameter Setting.

4.1 Dataset

Due to the specificity of conferences, publicly available conference dialogue datasets are extremely scarce. The AMC corpus (AliMeeting4MUG Corpus) (Zhang et al., 2023) is currently the largest publicly available conference dialogue data set, and we used its 589 publicly segmented conference recordings for our experiments.

Furthermore, to validate the generalizability of our method, we collected and manually annotated 1553 long dialogue texts (MDT1553) covering multiple domains and topics for experimentation. In addition to conferences, MDT1553 also includes various fields such as broadcasts, live streams, speeches, and TV programs, covering multiple topics (Figure 7), complementing the AMC corpus. Statistical data for both datasets are shown in Table 5.

4.2 Evaluation Metric

Traditional text segmentation metrics \( P_k \) (Beeferman et al., 1999) and \( WD \) (Pevzner and Hearst, 2002) are sensitive to paragraph length and provide unreasonable assessments of various types of errors (Fournier and Inkpen, 2012; Fournier, 2013; Diaz and Ouyang, 2022), making them unsuitable for accurately evaluating long dialogue text segmentation tasks. \( B \) (Fournier, 2013) uses edit distance as a penalty term, improving the shortcomings of traditional metrics. \( A \) (Diaz and Ouyang, 2022) is based on paragraph matching and uses the Jaccard Index to calculate closeness, which is associated with our 1DOD task. Additionally, it is length-
independent, provides continuous penalties for segmentation boundary shifts, and applies appropriate penalties for different types of errors, which we believe better matches human evaluation methods for long dialogue texts. Therefore, we primarily rely on the \( A \) metric for evaluating our experiments, but we will also provide scores for \( P_k, WD \), and \( B \) for reference. For \( P_k \) and \( WD \), the sliding window size and the maximum distance for boundary consistency in \( B \) are set to half the average real paragraph length. For calculating the \( A \) index, we use the original code, while the rest use the segeval.

4.3 Baseline Models
The average length of the dialogue data exceeds 10000, which is longer than the maximum length supported by mainstream language models. To better adapt to long contexts, we experimented with two Chinese language models, Longformer (Beltagy et al., 2020) and PoNet (Tan et al., 2022), which support lengths up to 4096. Notably, the experimental results in Table 1 show that PoNet outperforms Longformer. Moreover, in the experiments, using either the Longformer or SeqModel-Longformer model failed to achieve effective segmentation, which we believe is due to the labels being too sparse for the model to be trained properly. Based on these results, we chose PoNet as the primary model for our experiments. For tail processing, we used Bert (Devlin et al., 2019) to determine whether to merge. Additionally, we selected results from other high-performance text segmentation models for comparison, where the context length for Cross-segment Bert was set at 256, and the settings for other models followed their original publications.

5 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.

5.1 Main Results
Tables 1 and 2 respectively show the results on the AMC test set and MDT1553. All models are supervised and trained only on the AMC training set, with the best model selected based on results from the AMC validation set. Our proposed IoU, DIoU, and ADIoU methods yield comparable results, with ADIoU performing best when using sentence-level balanced labels, and IoU performing best when using token-level balanced labels.

Models based on Bert perform poorly because they are limited by length and imbalanced segmentation labels. Furthermore, the quality of sentence embedding in hierarchical models is very important. Bert models are mainly pretrained on classification datasets, and their sentence embedding representations differ significantly from the requirements of text segmentation tasks. TSSP+CSSL results are poor, which we believe is due to the complexity of long dialogues. The inherent noise and thematic nestedness in dialogue texts make data augmentation and contrastive learning lose their original significance. SeqModel shows a significant improvement over the base model, but the imbalance in labels still restricts the performance of the model. Our 1DOD STS method based on SeqModel and significantly improves over SeqModel, with increases of 11.87 and 10.86 on the \( A \) and \( B \) respectively, thus demonstrating the effectiveness of balanced labels and 1DOD methods.

MDT1553 includes more diverse dialogue data, which can reflect the model’s transferability across different dialogue topics. Without fine-tuning, we directly tested the MDT1553 dataset, and the best results of 1DOD showed significant improvements over SeqModel, with increases of 10.82 and 8.78 on the \( A \) and \( B \) respectively, still demonstrating substantial progress and exhibiting excellent generalization capabilities.

5.2 Ablation Study
Table 3 shows the results of the ablation studies. The accuracy of segmentation significantly improved just by adding balanced labels, but note that the \( 1 - P_k \) and \( 1 - WD \) slightly decreased, which we believe could be due to an increase in near-misses errors as the classification task shifted from predicting segmentation points to paragraphs; when 1DOD task was added, the model’s performance improved again. Horizontally comparing, token-level balanced labels outperformed sentence-level because they introduced more supervision signals; vertically comparing, IoU and ADIoU performed better than DIoU, indicating that IoU loss was sufficient, and ADIoU performed better as it balanced between target and background paragraphs. Finally, appropriate tail processing compensated for the shortcomings of 1DOD, further improving the accuracy of STS based on the existing results.
<table>
  <tr>
    <th>Methods</th>
    <th>A</th>
    <th>B</th>
    <th>1 − WD</th>
    <th>1 − \( P_k \)</th>
  </tr>
  <tr>
    <td>C99 (Choi, 2000)</td>
    <td>15.07</td>
    <td>4.85</td>
    <td>46.94</td>
    <td>51.80</td>
  </tr>
  <tr>
    <td>TopicTiling (Riedl and Biemann, 2012)</td>
    <td>19.03</td>
    <td>9.88</td>
    <td>45.02</td>
    <td>51.43</td>
  </tr>
  <tr>
    <td><i>Transformer<sup>2</sup><sub>BERT</sub></i> (Lo et al., 2021)</td>
    <td>32.92</td>
    <td>21.61</td>
    <td>53.89</td>
    <td>56.07</td>
  </tr>
  <tr>
    <td>Cross-segment BERT (Lukasik et al., 2020)</td>
    <td>35.20</td>
    <td>20.76</td>
    <td>53.65</td>
    <td>55.46</td>
  </tr>
  <tr>
    <td>PoNet (Tan et al., 2022)</td>
    <td>43.12</td>
    <td>31.17</td>
    <td>56.99</td>
    <td>59.98</td>
  </tr>
  <tr>
    <td>PoNet+TSSP+CSSL (Yu et al., 2023)</td>
    <td>42.48</td>
    <td>30.16</td>
    <td>56.21</td>
    <td>58.79</td>
  </tr>
  <tr>
    <td>SeqModel-PoNet (Zhang et al., 2021)</td>
    <td>47.84</td>
    <td>37.61</td>
    <td>56.83</td>
    <td>58.92</td>
  </tr>
  <tr>
    <td>1DOD-Longformer-ADIoU (token-level)</td>
    <td>58.41</td>
    <td>43.88</td>
    <td>58.34</td>
    <td>58.78</td>
  </tr>
  <tr>
    <td>1DOD-PoNet-IoU (token-level)</td>
    <td><b>59.71</b></td>
    <td><b>48.47</b></td>
    <td><b>61.18</b></td>
    <td><b>62.15</b></td>
  </tr>
</table>

Table 1: Comparative experimental results based on the AMC dataset. We only listed the best results of 1DOD-Longformer and 1DOD-PoNet models, and it can be seen that our method significantly outperforms other text segmentation models. All metrics are better when higher.

<table>
  <tr>
    <th>Balanced Labels</th>
    <th>Methods</th>
    <th>A</th>
    <th>B</th>
    <th>1 − WD</th>
    <th>1 − \( P_k \)</th>
  </tr>
  <tr>
    <td rowspan="7">Sentence-level</td>
    <td>C99</td>
    <td>10.07</td>
    <td>2.23</td>
    <td>51.28</td>
    <td>54.30</td>
  </tr>
  <tr>
    <td>TopicTiling</td>
    <td>10.72</td>
    <td>2.59</td>
    <td>52.84</td>
    <td>54.22</td>
  </tr>
  <tr>
    <td><i>Transformer<sup>2</sup><sub>BERT</sub></i></td>
    <td>28.47</td>
    <td>16.21</td>
    <td>57.97</td>
    <td>58.24</td>
  </tr>
  <tr>
    <td>Cross-segment BERT</td>
    <td>30.51</td>
    <td>16.16</td>
    <td>57.22</td>
    <td>56.69</td>
  </tr>
  <tr>
    <td>PoNet</td>
    <td>36.83</td>
    <td>27.78</td>
    <td><b>60.28</b></td>
    <td><b>61.35</b></td>
  </tr>
  <tr>
    <td>PoNet+TSSP+CSSL</td>
    <td>36.43</td>
    <td>27.21</td>
    <td>59.49</td>
    <td>60.94</td>
  </tr>
  <tr>
    <td>SeqModel-PoNet</td>
    <td>39.74</td>
    <td>29.41</td>
    <td>58.19</td>
    <td>59.88</td>
  </tr>
  <tr>
    <td rowspan="4">Token-level</td>
    <td>w/o 1DOD</td>
    <td>47.91</td>
    <td>35.27</td>
    <td>52.48</td>
    <td>54.10</td>
  </tr>
  <tr>
    <td>1DOD-PoNet-IoU</td>
    <td>48.30</td>
    <td>34.91</td>
    <td>54.76</td>
    <td>56.07</td>
  </tr>
  <tr>
    <td>1DOD-PoNet-DIoU</td>
    <td>49.77</td>
    <td>36.75</td>
    <td>57.29</td>
    <td>58.54</td>
  </tr>
  <tr>
    <td>1DOD-PoNet-ADIoU</td>
    <td>49.13</td>
    <td>35.91</td>
    <td>57.63</td>
    <td>58.78</td>
  </tr>
  <tr>
    <td rowspan="4">Token-level</td>
    <td>w/o 1DOD</td>
    <td>48.28</td>
    <td>35.42</td>
    <td>53.38</td>
    <td>54.94</td>
  </tr>
  <tr>
    <td>1DOD-PoNet-IoU</td>
    <td>50.35</td>
    <td>38.24</td>
    <td>57.70</td>
    <td>59.13</td>
  </tr>
  <tr>
    <td>1DOD-PoNet-DIoU</td>
    <td>50.39</td>
    <td><b>38.62</b></td>
    <td>57.45</td>
    <td>59.07</td>
  </tr>
  <tr>
    <td>1DOD-Longformer-ADIoU</td>
    <td>46.38</td>
    <td>31.25</td>
    <td>52.94</td>
    <td>53.99</td>
  </tr>
  <tr>
    <td>1DOD-PoNet-ADIoU</td>
    <td><b>50.56</b></td>
    <td>38.19</td>
    <td>58.97</td>
    <td>60.22</td>
  </tr>
</table>

Table 2: Comparative experimental results for MDT1553. Models are trained only on the AMC training set to demonstrate generalization capabilities. For the 1DOD-Longformer, we only tested its best model.

<table>
  <tr>
    <th rowspan="2">Methods</th>
    <th colspan="4">Sentence-level</th>
    <th colspan="4">Token-level</th>
  </tr>
  <tr>
    <th>A</th>
    <th>B</th>
    <th>1 − WD</th>
    <th>1 − \( P_k \)</th>
    <th>A</th>
    <th>B</th>
    <th>1 − WD</th>
    <th>1 − \( P_k \)</th>
  </tr>
  <tr>
    <td>\( \mathcal{L}_{cls} \)</td>
    <td>55.46</td>
    <td>45.21</td>
    <td>55.67</td>
    <td>56.77</td>
    <td>55.63</td>
    <td>45.58</td>
    <td>55.81</td>
    <td>56.98</td>
  </tr>
  <tr>
    <td>+tail process</td>
    <td>56.23</td>
    <td>45.63</td>
    <td>56.60</td>
    <td>57.67</td>
    <td>56.24</td>
    <td>45.82</td>
    <td>56.64</td>
    <td>57.78</td>
  </tr>
  <tr>
    <td>\( \mathcal{L}_{cls} + \mathcal{L}_{1D\ bbox-IoU} \)</td>
    <td>58.70</td>
    <td>46.73</td>
    <td>59.19</td>
    <td>59.80</td>
    <td>59.17</td>
    <td>48.21</td>
    <td>60.29</td>
    <td>61.26</td>
  </tr>
  <tr>
    <td>+tail process</td>
    <td><b>59.69</b></td>
    <td><b>47.20</b></td>
    <td>60.26</td>
    <td>60.87</td>
    <td><b>59.71</b></td>
    <td><b>48.47</b></td>
    <td>61.18</td>
    <td><b>62.15</b></td>
  </tr>
  <tr>
    <td>\( \mathcal{L}_{cls} + \mathcal{L}_{1D\ bbox-DIoU} \)</td>
    <td>58.50</td>
    <td>46.57</td>
    <td>59.08</td>
    <td>59.81</td>
    <td>58.63</td>
    <td>47.65</td>
    <td>59.64</td>
    <td>60.52</td>
  </tr>
  <tr>
    <td>+tail process</td>
    <td>59.11</td>
    <td>46.72</td>
    <td>59.92</td>
    <td>60.64</td>
    <td>59.23</td>
    <td>47.97</td>
    <td>60.61</td>
    <td>61.48</td>
  </tr>
  <tr>
    <td>\( \mathcal{L}_{cls} + \mathcal{L}_{1D\ bbox-ADIoU} \)</td>
    <td>58.88</td>
    <td>46.21</td>
    <td>59.83</td>
    <td>60.50</td>
    <td>59.19</td>
    <td>46.80</td>
    <td>60.43</td>
    <td>61.08</td>
  </tr>
  <tr>
    <td>+tail process</td>
    <td>59.29</td>
    <td>46.22</td>
    <td><b>60.67</b></td>
    <td><b>61.32</b></td>
    <td>59.62</td>
    <td>46.83</td>
    <td><b>61.29</b></td>
    <td>61.92</td>
  </tr>
</table>

Table 3: Ablation studies based on PoNet and AMC dataset, where \( \mathcal{L}_{cls} \) indicates that the model was trained only on the classification task. The general method is shown in Table 1 "PoNet".
5.3 Stability Analysis

5.3.1 Stability of Training

![Evaluation results on the validation set during the training of SeqModel-PoNet (left) and 1DOD-PoNet-IoU (sentence-level, right), using the same training parameters.](page_120_186_495_246.png)

Figure 5: Evaluation results on the validation set during the training of SeqModel-PoNet (left) and 1DOD-PoNet-IoU (sentence-level, right), using the same training parameters.

In the experimental section, we selected the best model based on the evaluation results of the validation set for assessment. During training, the evaluation results of SeqModel and 1DOD for each round on the validation set are shown in Figure 5. The 1DOD model demonstrated good stability during training (with an \( A \) movement range of about 3), converging to the best level by only 2 epochs, and achieving higher evaluation scores than SeqModel, whereas SeqModel exhibited fluctuations in the early stages of training (with an \( A \) movement range of about 20). The fundamental cause of these fluctuations is the imbalance in training labels.

5.3.2 Stability of Segmentation

Tables 4 and Figure 6 display the statistical data and distribution of segment paragraph lengths for different models. The model’s direct segmentation results differ most significantly from the actual labels due to the imbalance of segmentation labels and the irrelevance of paragraph length in sentence classification tasks; the self-adaptive sliding window model slightly improved this by the limitation of the window size, but it was still insufficient as the model did not learn paragraph length information. Models with added balanced labels resulted in overly stable segmentation outcomes, still differing from the actual distribution. The 1DOD method, by incorporating a 1D bounding-box regression loss, enhances the model’s understanding of the “target area range”, achieving an effect similar to that of Yoo and Kim (2024), who directly incorporated paragraph length information into the loss function, yet in a more intuitive manner. This makes the 1DOD model’s predictions more consistent with the actual labels’ distribution.

<table>
  <tr>
    <th></th>
    <th colspan="2">AMC-test</th>
    <th colspan="2">MDT1553</th>
  </tr>
  <tr>
    <th></th>
    <th>Avg</th>
    <th>Std</th>
    <th>Avg</th>
    <th>Std</th>
  </tr>
  <tr>
    <td>Label</td>
    <td><b>31.25</b></td>
    <td><b>19.20</b></td>
    <td><b>31.10</b></td>
    <td><b>29.23</b></td>
  </tr>
  <tr>
    <td>PoNet</td>
    <td>41.78</td>
    <td>46.34</td>
    <td>74.67</td>
    <td>76.89</td>
  </tr>
  <tr>
    <td>SeqModel</td>
    <td>35.15</td>
    <td>35.21</td>
    <td>45.20</td>
    <td>44.70</td>
  </tr>
  <tr>
    <td>SeqModel<sub>TBL</sub></td>
    <td>26.34</td>
    <td>15.40</td>
    <td>29.36</td>
    <td>16.57</td>
  </tr>
  <tr>
    <td>1DOD-IoU</td>
    <td>29.70</td>
    <td>16.46</td>
    <td>31.85</td>
    <td>18.51</td>
  </tr>
  <tr>
    <td>1DOD-DIoU</td>
    <td>29.92</td>
    <td>16.09</td>
    <td>31.08</td>
    <td>17.83</td>
  </tr>
  <tr>
    <td>1DOD-ADIoU</td>
    <td>32.51</td>
    <td>19.14</td>
    <td>34.33</td>
    <td>23.52</td>
  </tr>
</table>

Table 4: The average and standard deviation of the paragraph lengths segmented by different models. ‘TBL’ refers to token-level balanced label.

![Comparison of segment paragraph length distributions for different models.](page_1012_1012_388_246.png)

Figure 6: Comparison of segment paragraph length distributions for different models.

6 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.

Limitations

During the segmentation process of streaming dialogue texts, the model may model the same text with different contexts multiple times, which reduces efficiency. Moreover, our method is suited for data where the paragraph length distribution follows a normal distribution, which is the case for most dialogue text data. For other special distributions (such as U-shaped or J-shaped) of dialogue texts, which is not the intended direction for STS as a preprocessing task for dialogue texts. For these special types of dialogue texts, our model is unable to achieve better results.
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A  Data Statistics and Distribution

<table>
  <tr>
    <th></th>
    <th>No.</th>
    <th>#Utts.</th>
    <th>#Pars.</th>
  </tr>
  <tr>
    <td rowspan="3">AMC</td>
    <td>Train</td>
    <td>295</td>
    <td>469.72</td>
    <td>11.06</td>
  </tr>
  <tr>
    <td>Validation</td>
    <td>65</td>
    <td>463.79</td>
    <td>11.32</td>
  </tr>
  <tr>
    <td>Test</td>
    <td>229</td>
    <td>326.79</td>
    <td>10.46</td>
  </tr>
  <tr>
    <td>MDT1553</td>
    <td>All</td>
    <td>1553</td>
    <td>495.99</td>
    <td>15.95</td>
  </tr>
</table>

Table 5: Data statistics of AMC and MDT1553 datasets. # means average.

![Pie chart showing topic distribution of MDT1553](page_384_670_670_314.png)

Figure 7: Topic distribution of MDT1553.

B  Parameter Setting

Our experiments were conducted on the PyTorch 2.0.0. During training, we used the AdamW optimizer, with a batch size and learning rate set at 2 and 5e-5, respectively; \( \alpha \) and \( \gamma \) were set at 0.75 and 2; \( \lambda_{SL1} \), \( \lambda_{IoU} \), and \( \lambda_{Dice} \) were set at 10, 0.5, and 0.5 respectively, under this setting, the balance between various losses can be maintained.

The hyperparameters for inference were obtained through experiments on the validation set, and since the AMC dataset and MDT1553 dataset are similar in length and format, the same set of parameters was used. The window size was set to 100, \( t_{stop} \) to 31, and \( t_{min} \) to 23. Our experiments were conducted on GeForce RTX-4090 GPUs and Linux OS.

C  Ablation Study based on Longformer

The experimental results are shown in Table 6.

D  Comparison with LLMs

We selected several mainstream online large language models for comparison (Table 7). We conducted experiments using the top 65 samples from the AMC test set, with all LLMs operating in a zero-shot setting.

E  Time Consumption Comparison

We compared the baseline model and 1DOD on the AMC test set in terms of average text segmentation time under identical hardware conditions and parameter settings (batch size=1), with the results shown in Table 8.
<table>
  <tr>
    <th rowspan="2">Methods</th>
    <th colspan="4">Sentence-level</th>
    <th colspan="4">Token-level</th>
  </tr>
  <tr>
    <th>A</th>
    <th>B</th>
    <th>1 − WD</th>
    <th>1 − \( P_k \)</th>
    <th>A</th>
    <th>B</th>
    <th>1 − WD</th>
    <th>1 − \( P_k \)</th>
  </tr>
  <tr>
    <td>\( \mathcal{L}_{cls} \)</td>
    <td>56.29</td>
    <td>42.33</td>
    <td>54.86</td>
    <td>55.43</td>
    <td>56.60</td>
    <td>41.25</td>
    <td>54.94</td>
    <td>55.40</td>
  </tr>
  <tr>
    <td>+tail process</td>
    <td>56.68</td>
    <td>42.24</td>
    <td>55.76</td>
    <td>56.32</td>
    <td>56.83</td>
    <td>41.06</td>
    <td>55.95</td>
    <td>56.41</td>
  </tr>
  <tr>
    <td>\( \mathcal{L}_{cls} + \mathcal{L}_{1D\ bbox-IoU} \)</td>
    <td>57.21</td>
    <td>42.91</td>
    <td>56.74</td>
    <td>57.29</td>
    <td>57.03</td>
    <td>43.68</td>
    <td>56.30</td>
    <td>56.91</td>
  </tr>
  <tr>
    <td>+tail process</td>
    <td><b>57.91</b></td>
    <td>43.17</td>
    <td><b>57.83</b></td>
    <td><b>58.37</b></td>
    <td>57.34</td>
    <td>43.53</td>
    <td>57.20</td>
    <td>57.80</td>
  </tr>
  <tr>
    <td>\( \mathcal{L}_{cls} + \mathcal{L}_{1D\ bbox-DIoU} \)</td>
    <td>56.91</td>
    <td>42.94</td>
    <td>55.93</td>
    <td>56.48</td>
    <td>57.10</td>
    <td>43.63</td>
    <td>56.52</td>
    <td>57.34</td>
  </tr>
  <tr>
    <td>+tail process</td>
    <td>57.58</td>
    <td>43.15</td>
    <td>57.02</td>
    <td>57.56</td>
    <td>57.65</td>
    <td>43.70</td>
    <td>57.54</td>
    <td>58.37</td>
  </tr>
  <tr>
    <td>\( \mathcal{L}_{cls} + \mathcal{L}_{1D\ bbox-ADIoU} \)</td>
    <td>56.57</td>
    <td>44.85</td>
    <td>56.11</td>
    <td>57.14</td>
    <td>58.08</td>
    <td><b>43.91</b></td>
    <td>57.33</td>
    <td>57.78</td>
  </tr>
  <tr>
    <td>+tail process</td>
    <td>57.25</td>
    <td><b>45.07</b></td>
    <td>57.34</td>
    <td>58.20</td>
    <td><b>58.41</b></td>
    <td>43.88</td>
    <td><b>58.34</b></td>
    <td><b>58.78</b></td>
  </tr>
</table>

Table 6: Ablation study based on longformer.

<table>
  <tr>
    <th>Methods</th>
    <th>A</th>
    <th>B</th>
    <th>1 − WD</th>
    <th>1 − \( P_k \)</th>
  </tr>
  <tr>
    <td>GLM-4</td>
    <td>24.59</td>
    <td>20.19</td>
    <td>18.16</td>
    <td>47.80</td>
  </tr>
  <tr>
    <td>Moonshot-v1-32k</td>
    <td>32.05</td>
    <td>26.75</td>
    <td>28.60</td>
    <td>48.89</td>
  </tr>
  <tr>
    <td>GPT-4 Turbo</td>
    <td>31.68</td>
    <td>26.37</td>
    <td>25.53</td>
    <td>47.76</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>38.21</td>
    <td>30.41</td>
    <td>34.71</td>
    <td>47.81</td>
  </tr>
  <tr>
    <td>Ours</td>
    <td><b>59.10</b></td>
    <td><b>48.08</b></td>
    <td><b>60.75</b></td>
    <td><b>61.84</b></td>
  </tr>
</table>

Table 7: Comparison results with online large models.

<table>
  <tr>
    <th>Methods</th>
    <th>Avg. Time (s)</th>
  </tr>
  <tr>
    <td>PoNet</td>
    <td>0.86</td>
  </tr>
  <tr>
    <td>SeqModel-PoNet</td>
    <td>1.47</td>
  </tr>
  <tr>
    <td>1DOD-PoNet-IoU (token-level)</td>
    <td>1.54</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>5.84</td>
  </tr>
</table>

Table 8: Experimental results of time consumption comparison.

Reviewer paper summaries: Paper presents approach to segmenting text (or, more precisely, dialogue transcript) streams into segments. The task is framed as one of finding a single boundary in a window of text, which when found then determines the start of the next window. The segmentation model is optimised on a combination of making a sentence-level decision ("is there a boundary after this sentence?") with a loss that takes the overall length of the segment into account.
Summary of strengths: - Paper makes interesting analogy between finding a segment in a sequence and detecting a bounding box in an image, framing the former task as a "1D bounding box regression".

- The streaming setup poses an interesting variation to the traditional NLP segmentation problem. The proposal to use a window that is restarted with every decision makes sense.
Summary of weaknesses: - I missed discussion of whether a strict segmentation into "paragraphs" is even appropriate for the task, or at least what falls by the side if one decides for it. It is well known that discourse is not well modelled as a sequence and is much better captured by graph-like structures (RST, QUD, etc.). Paper doesn't really discuss its notion of segmentation at all (let alone relate it to any research on discourse).

- It wasn't clear to me whether the analogy between segmentation and bounding box detecting extends beyond motivating the metric. I don't see that the methods used are too similar to what is done in Yolo for example?

- My main problem with the paper is readability. I think the basic ideas in the paper are sound and interesting, but the overall impression is a bit confusing. Way too much emphasis is given to explaining the task (which is relatively straightforward) and the problem of imbalance (which occurs in almost every classification task that holds any interest), and not enough space is devoted to explaining clearly which models were tested, what exactly the training data was (including giving examples and details about how it was annotated), and how the evaluation metrics (that are just listed in 4.2) are computed.
Comments, suggestions and typos: - Maybe I missed it in the paper, but did you look at the potential for error percolation? If the new window starts where the previous boundary was set, what happens if that decision was wrong?
Overall assessment scores: 3.5