Text segmentation is a fundamental task in natural language processing, where
documents are split into contiguous sections. However, prior research in this
area has been constrained by limited datasets, which are either small in scale,
synthesized, or only contain well-structured documents. In this paper, we
address these limitations by introducing a novel benchmark YTSeg focusing on
spoken content that is inherently more unstructured and both topically and
structurally diverse. As part of this work, we introduce an efficient
hierarchical segmentation model MiniSeg, that outperforms state-of-the-art
baselines. Lastly, we expand the notion of text segmentation to a more
practical "smart chaptering" task that involves the segmentation of
unstructured content, the generation of meaningful segment titles, and a
potential real-time application of the models.

本文介绍了一个新的评测基准 YTSeg，着重于非结构化的语音内容，提出了一种高效的层次分割模型 MiniSeg，并对比了现有的基准方法。此外，还将文本分割的概念扩展到实际的 “智能章节切割” 任务中，包括非结构化内容的分割、生成有意义的片段标题以及模型的潜在实时应用。