Balancing Lexical and Semantic Quality in Abstractive Summarization
Jeewoo Sul Yong Suk Choi
An important problem of the sequence-to-sequence neural models widely used in
abstractive summarization is exposure bias. To alleviate this problem,
re-ranking systems have been applied in recent years. Despite some performance
improvements, this approach remains underexplored. Previous works have mostly
specified the rank through the ROUGE score and aligned candidate summaries, but
there can be quite a large gap between the lexical overlap metric and semantic
similarity. In this paper, we propose a novel training method in which a
re-ranker balances the lexical and semantic quality. We further newly define
false positives in ranking and present a strategy to reduce their influence.
Experiments on the CNN/DailyMail and XSum datasets show that our method can
estimate the meaning of summaries without seriously degrading the lexical
aspect. More specifically, it achieves an 89.67 BERTScore on the CNN/DailyMail
dataset, reaching new state-of-the-art performance. Our code is publicly
available at https://github.com/jeewoo1025/BalSum.