  Advancement in large pretrained language models has significantly improved
their performance for conditional language generation tasks including
summarization albeit with hallucinations. To reduce hallucinations,
conventional methods proposed improving beam search or using a fact checker as
a postprocessing step. In this paper, we investigate the use of the Natural
Language Inference (NLI) entailment metric to detect and prevent hallucinations
in summary generation. We propose an NLI-assisted beam re-ranking mechanism by
computing entailment probability scores between the input context and
summarization model-generated beams during saliency-enhanced greedy decoding.
Moreover, a diversity metric is introduced to compare its effectiveness against
vanilla beam search. Our proposed algorithm significantly outperforms vanilla
beam decoding on XSum and CNN/DM datasets.
