Unsupervised Topic Segmentation of Meetings with BERT Embeddings [article]

Alessandro Solbiati, Kevin Heffernan, Georgios Damaskinos, Shivani Poddar, Shubham Modi, Jacques Cali
2021 arXiv   pre-print
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
more » ... g unsupervised approaches applied to two popular datasets for meeting transcripts.
arXiv:2106.12978v1 fatcat:dfyx4w7gobcfflh5sjfvgaus3u