• Corpus ID: 235624000

Unsupervised Topic Segmentation of Meetings with BERT Embeddings

@article{Solbiati2021UnsupervisedTS,
  title={Unsupervised Topic Segmentation of Meetings with BERT Embeddings},
  author={Alessandro Solbiati and Kevin Heffernan and Georgios Damaskinos and Shivani Poddar and Shubham Modi and Jacques Cal{\`i}},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.12978}
}
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… 
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