In this paper, we introduce a new training framework, called LaCE (Label Consistency Explorer), that leverages label consistency among paragraph summaries.
it encourages label consistency during end-to-end training of a neural model, allowing consistency bias to improve the model itself, rather than be enforced in a post-processing step, e.g., posterior regularization (Ganchev et al., 2010).