Similar to<cite> (Koshorek et al. 2018)</cite> , CATS' main learning objective is a binary sentence-level segmentation prediction. However, CATS augments the segmentation objective with an auxiliary coherence-based objec-tive which pushes the model to predict higher coherence for original text snippets than for corrupt (i.e., fake) sentence sequences. We empirically show (1) that even without the auxiliary coherence objective, the Two-Level Transformer model for Text Segmentation (TLT-TS) yields state-of-the-art performance across multiple benchmarks, (2) that the full CATS model, with the auxiliary coherence modeling, further significantly improves the segmentation, and (3) that both TLT-TS and CATS are robust in domain transfer.