ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization

TL;DR

ExtraPhrase is a cost-effective data augmentation method that enhances abstractive summarization by generating diverse pseudo training data through extractive summarization and paraphrasing, especially benefiting low-resource scenarios.

Contribution

This paper introduces ExtraPhrase, a novel low-cost data augmentation technique that improves summarization performance and outperforms existing methods like back-translation and self-training.

Findings

Improves ROUGE scores by over 0.50 points with augmentation.

Outperforms back-translation and self-training methods.

Highly effective in low-resource settings.

Abstract

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.