  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.
