English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Topic segmentation of meetings is the task of dividing multi-person meetingtranscripts into topic blocks. Supervised approaches to the problem have provenintractable due to the difficulties in collecting and accurately annotatinglarge datasets. In this paper we show how previous unsupervised topicsegmentation methods can be improved using pre-trained neural architectures. Weintroduce an unsupervised approach based on BERT embeddings that achieves a15.5% reduction in error rate over existing unsupervised approaches applied totwo popular datasets for meeting transcripts.

Unsupervised Topic Segmentation of Meetings with BERT Embeddings