Similarly, a recently proposed state-of-the-art supervised neural segmentation model<cite> (Koshorek et al. 2018</cite> ) directly learns to predict binary sentence-level segmentation decisions and has no explicit mechanism for modeling coherence. In this work, in contrast, we propose a supervised neural model for text segmentation that explicitly takes coherence into account: we augment the segmentation prediction objective with an auxiliary coherence modeling objective. Our proposed model, dubbed Coherence-Aware Text Segmentation (CATS), encodes a sentence sequence using two hierarchically connected Transformer networks (Vaswani et al. 2017; Devlin et al. 2018 ).