  From organizing recorded videos and meetings into chapters, to breaking down
large inputs in order to fit them into the context window of commoditized Large
Language Models (LLMs), topic segmentation of large transcripts emerges as a
task of increasing significance. Still, accurate segmentation presents many
challenges, including (a) the noisy nature of the Automatic Speech Recognition
(ASR) software typically used to obtain the transcripts, (b) the lack of
diverse labeled data and (c) the difficulty in pin-pointing the ground-truth
number of segments. In this work we present TreeSeg, an approach that combines
off-the-shelf embedding models with divisive clustering, to generate
hierarchical, structured segmentations of transcripts in the form of binary
trees. Our approach is robust to noise and can handle large transcripts
efficiently. We evaluate TreeSeg on the ICSI and AMI corpora, demonstrating
that it outperforms all baselines. Finally, we introduce TinyRec, a small-scale
corpus of manually annotated transcripts, obtained from self-recorded video
sessions.
