Finally,<cite> Koshorek et al. (2018)</cite> identify Wikipedia as a free large-scale source of manually segmented texts that can be used to train a supervised segmentation model. They train a neural model that hierarchically combines two bidirectional LSTM networks and report massive improvements over unsupervised segmentation on a range of evaluation datasets. The model we presented in this work has a similar hierarchical architecture, but uses Transfomer networks instead of recurrent encoders. Crucially, CATS additionally defines an auxiliary coherence objective, which is coupled with the (primary) segmentation objective in a multi-task learning model.