  Modern abstractive summarization models often generate summaries that contain
hallucinated or contradictory information. In this paper, we propose a simple
but effective contrastive learning framework that incorporates recent
developments in reward learning and factuality metrics. Empirical studies
demonstrate that the proposed framework enables summarization models to learn
from feedback of factuality metrics using contrastive reward learning, leading
to more factual summaries by human evaluations. This suggests that further
advances in learning and evaluation algorithms can feed directly into providing
more factual summaries.

Computation and Language;Artificial Intelligence