Improving factual consistency of abstractive summarization has been a widely
studied topic. However, most of the prior works on training factuality-aware
models have ignored the negative effect it has on summary quality. We propose
EFACTSUM (i.e., Effective Factual Summarization), a candidate summary
generation and ranking technique to improve summary factuality without
sacrificing summary quality. We show that using a contrastive learning
framework with our refined candidate summaries leads to significant gains on
both factuality and similarity-based metrics. Specifically, we propose a
ranking strategy in which we effectively combine two metrics, thereby
preventing any conflict during training. Models trained using our approach show
up to 6 points of absolute improvement over the base model with respect to
FactCC on XSUM and 11 points on CNN/DM, without negatively affecting either
similarity-based metrics or absractiveness.

本文提出了一种名为 EFACTSUM 的候选摘要生成和排名技术，旨在在不牺牲摘要质量的前提下提高摘要的事实性。通过使用对比学习框架并结合两个度量，训练出的模型在 XSUM 和 CNN / DM 上相对于基本模型均有显著的事实性和相似性改进。