Text segmentation aims to divide text into contiguous, semantically coherent
segments, while segment labeling deals with producing labels for each segment.
Past work has shown success in tackling segmentation and labeling for documents
and conversations. This has been possible with a combination of task-specific
pipelines, supervised and unsupervised learning objectives. In this work, we
propose a single encoder-decoder neural network that can handle long documents
and conversations, trained simultaneously for both segmentation and segment
labeling using only standard supervision. We successfully show a way to solve
the combined task as a pure generation task, which we refer to as structured
summarization. We apply the same technique to both document and conversational
data, and we show state of the art performance across datasets for both
segmentation and labeling, under both high- and low-resource settings. Our
results establish a strong case for considering text segmentation and segment
labeling as a whole, and moving towards general-purpose techniques that don't
depend on domain expertise or task-specific components.

提出一种单一的编码器 - 解码器神经网络方法，通过结构化总结的形式同时训练文档和对话数据的文本切片和标签任务，并成功展示了该方法在所属领域的顶尖性能以及其重要性。

结构化摘要：统一的文本分段和段落标注作为生成任务

Structured Summarization: Unified Text Segmentation and Segment Labeling as a Generation Task

Despite the importance of unsupervised object detection, to the best of our
knowledge, there is no previous work addressing this problem. One main issue,
widely known to the community, is that object boundaries derived only from 2D
image appearance are ambiguous and unreliable. To address this, we exploit
LiDAR clues to aid unsupervised object detection. By exploiting the 3D scene
structure, the issue of localization can be considerably mitigated. We further
identify another major issue, seldom noticed by the community, that the
long-tailed and open-ended (sub-)category distribution should be accommodated.
In this paper, we present the first practical method for unsupervised object
detection with the aid of LiDAR clues. In our approach, candidate object
segments based on 3D point clouds are firstly generated. Then, an iterative
segment labeling process is conducted to assign segment labels and to train a
segment labeling network, which is based on features from both 2D images and 3D
point clouds. The labeling process is carefully designed so as to mitigate the
issue of long-tailed and open-ended distribution. The final segment labels are
set as pseudo annotations for object detection network training. Extensive
experiments on the large-scale Waymo Open dataset suggest that the derived
unsupervised object detection method achieves reasonable accuracy compared with
that of strong supervision within the LiDAR visible range. Code shall be
released.

通过利用 LiDAR 数据辅助进行无监督的二维对象检测，同时采用基于 3D 点云和 2D 图像特征的迭代分割标签网络对候选对象进行标签生成和训练，从而解决了之前存在的问题，得到了较为合理的检测结果。