We consider the task of unsupervised lecture segmentation. We formalize segmentation as a graph-partitioning task that optimizes the normalized cut criterion. Our approach moves beyond localized comparisons and takes into account longrange cohesion dependencies. Our results demonstrate that global analysis improves the segmentation accuracy and is robust in the presence of speech recognition errors.
The specific model we leverage REF considers not just local but also long-range cohesion dependencies, based on a graph partitioning framework, which improves segmentation accuracy and is robust to speech recognition errors.
Minimum Cut Model For Spoken Lecture Segmentation
International Conference On Computational Linguistics And Annual Meeting Of The Association For Computational Linguistics
Computer Science