Neural models trained with large amount of parallel data have achieved
impressive performance in abstractive summarization tasks. However, large-scale
parallel corpora are expensive and challenging to construct. In this work, we
introduce a low-cost and effective strategy, ExtraPhrase, to augment training
data for abstractive summarization tasks. ExtraPhrase constructs pseudo
training data in two steps: extractive summarization and paraphrasing. We
extract major parts of an input text in the extractive summarization step, and
obtain its diverse expressions with the paraphrasing step. Through experiments,
we show that ExtraPhrase improves the performance of abstractive summarization
tasks by more than 0.50 points in ROUGE scores compared to the setting without
data augmentation. ExtraPhrase also outperforms existing methods such as
back-translation and self-training. We also show that ExtraPhrase is
significantly effective when the amount of genuine training data is remarkably
small, i.e., a low-resource setting. Moreover, ExtraPhrase is more
cost-efficient than the existing approaches.

本文介绍了一种针对抽象摘要任务的低成本、高效的数据扩充策略，ExtraPhrase，通过提取性摘要和改写两个步骤来构建伪训练数据，实验证明 ExtraPhrase 相比不使用数据扩充的设置在 ROUGE 指标上提高了超过 0.50 分，并且超过了现有的方法，尤其在低资源设置下表现出明显提升，同时 ExtraPhrase 比现有方法更加成本效益高。