Unsupervised Topic Segmentation of Meetings with BERT Embeddings

Understand

Topic segmentation of meetings is the task of dividing multi-person meeting transcripts into topic blocks.

• Supervised approaches to the problem have proven intractable due to the difficulties in collecting and accurately annotating large datasets.
• In this paper we show how previous unsupervised topic segmentation methods can be improved using pre-trained neural architectures.
• We introduce an unsupervised approach based on BERT embeddings that achieves a 15.5% reduction in error rate over existing unsupervised approaches applied to two popular datasets for meeting transcripts.