Improving Factuality of Abstractive Summarization without Sacrificing
  Summary Quality
Tanay Dixit Fei Wang Muhao Chen
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.