  Despite the recent advances in abstractive text summarization, current
summarization models still suffer from generating factually inconsistent
summaries, reducing their utility for real-world application. We argue that the
main reason for such behavior is that the summarization models trained with
maximum likelihood objective assign high probability to plausible sequences
given the context, but they often do not accurately rank sequences by their
consistency. In this work, we solve this problem by calibrating the likelihood
of model generated sequences to better align with a consistency metric measured
by natural language inference (NLI) models. The human evaluation study and
automatic metrics show that the calibrated models generate more consistent and
higher-quality summaries. We also show that the models trained using our method
return probabilities that are better aligned with the NLI scores, which
significantly increase reliability of summarization models.
