  We present HyperSeg, a hyperdimensional computing (HDC) approach to
unsupervised dialogue topic segmentation. HDC is a class of vector symbolic
architectures that leverages the probabilistic orthogonality of randomly drawn
vectors at extremely high dimensions (typically over 10,000). HDC generates
rich token representations through its low-cost initialization of many
unrelated vectors. This is especially beneficial in topic segmentation, which
often operates as a resource-constrained pre-processing step for downstream
transcript understanding tasks. HyperSeg outperforms the current
state-of-the-art in 4 out of 5 segmentation benchmarks -- even when baselines
are given partial access to the ground truth -- and is 10 times faster on
average. We show that HyperSeg also improves downstream summarization accuracy.
With HyperSeg, we demonstrate the viability of HDC in a major language task. We
open-source HyperSeg to provide a strong baseline for unsupervised topic
segmentation.
