State-of-the-art abstractive summarization models generally rely on extensive labeled data, which lowers their generalization ability on domains where such data are not available. In this paper, we present a study of domain adaptation for the abstractive summarization task across six diverse target domains in a low-resource setting. Specifically, we investigate the second phase of pre-training on large-scale generative models under three different settings: 1) source domain pre-training; 2) domain-adaptive pre-training; and 3) task-adaptive pre-training. Experiments show that the effectiveness of pre-training is correlated with the similarity between the pre-training data and the target domain task. Moreover, we find that continuing pre-training could lead to the pre-trained model's catastrophic forgetting, and a learning method with less forgetting can alleviate this issue. Furthermore, results illustrate that a huge gap still exists between the low-resource and high-resource settings, which highlights the need for more advanced domain adaptation methods for the abstractive summarization task.

本研究探讨了在低资源环境下，针对六个不同领域的抽象摘要任务中，利用大规模生成模型进行领域自适应预训练的有效性。实验表明，预训练的有效性与预训练数据与目标任务的相似度相关，同时发现预训练会导致预训练模型的灾难性遗忘，但使用一种遗忘较少的学习方法可以缓解这个问题。此外，结果表明低资源和高资源的差距仍然很大，需要更先进的领域自适应方法来解决抽象摘要任务中的问题。

AdaptSum: 面向低资源域自适应的摘要生成