A robust summarization system should be able to capture the gist of the document, regardless of the specific word choices or noise in the input. In this work, we first explore the summarization models' robustness against perturbations including word-level synonym substitution and noise. To create semantic-consistent substitutes, we propose a SummAttacker, which is an efficient approach to generating adversarial samples based on language models. Experimental results show that state-of-the-art summarization models have a significant decrease in performance on adversarial and noisy test sets. Next, we analyze the vulnerability of the summarization systems and explore improving the robustness by data augmentation. Specifically, the first brittleness factor we found is the poor understanding of infrequent words in the input. Correspondingly, we feed the encoder with more diverse cases created by SummAttacker in the input space. The other factor is in the latent space, where the attacked inputs bring more variations to the hidden states. Hence, we construct adversarial decoder input and devise manifold softmixing operation in hidden space to introduce more diversity. Experimental results on Gigaword and CNN/DM datasets demonstrate that our approach achieves significant improvements over strong baselines and exhibits higher robustness on noisy, attacked, and clean datasets.
|Title of host publication
|Association for Computational Linguistics (ACL)
|Number of pages
|Published - 2023
|61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada
Duration: Jul 9 2023 → Jul 14 2023
|Proceedings of the Annual Meeting of the Association for Computational Linguistics
|61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
|07/9/23 → 07/14/23
Bibliographical notePublisher Copyright:
© 2023 Association for Computational Linguistics.
ASJC Scopus subject areas
- Computer Science Applications
- Linguistics and Language
- Language and Linguistics