  We apply the Adversarial NLI dataset to train the NLI model and show that the
model has the potential to enhance factual correctness in abstract
summarization. We follow the work of Falke et al. (2019), which rank multiple
generated summaries based on the entailment probabilities between an source
document and summaries and select the summary that has the highest entailment
probability. The authors' earlier study concluded that current NLI models are
not sufficiently accurate for the ranking task. We show that the Transformer
models fine-tuned on the new dataset achieve significantly higher accuracy and
have the potential of selecting a coherent summary.
