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.

HyperSeg 是一种基于超维计算（HDC）的无监督对话主题分割方法，它通过在非常高的维度上随机选择向量的概率正交性来生成丰富的词元表示，从而在话题分割和下游的摘要任务中取得了显著的性能提升，同时具备更快的速度。