ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization
This addresses the challenge of expensive and scarce training data for abstractive summarization, particularly in low-resource settings, though it is incremental as it builds on existing data augmentation techniques.
The paper tackles the problem of limited parallel data for abstractive summarization by introducing ExtraPhrase, a low-cost data augmentation strategy that constructs pseudo training data through extractive summarization and paraphrasing, resulting in improvements of over 0.50 points in ROUGE scores compared to no augmentation and outperforming existing methods like back-translation.
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.