AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization

Tiezheng Yu, Zihan Liu, Pascale Fung


Abstract
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.
Anthology ID:
2021.naacl-main.471
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5892–5904
Language:
URL:
https://aclanthology.org/2021.naacl-main.471
DOI:
10.18653/v1/2021.naacl-main.471
Bibkey:
Cite (ACL):
Tiezheng Yu, Zihan Liu, and Pascale Fung. 2021. AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5892–5904, Online. Association for Computational Linguistics.
Cite (Informal):
AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization (Yu et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.471.pdf
Optional supplementary code:
 2021.naacl-main.471.OptionalSupplementaryCode.zip
Video:
 https://aclanthology.org/2021.naacl-main.471.mp4
Code
 TysonYu/AdaptSum
Data
CNN/Daily MailGLUEReddit TIFU