Improving the Robustness of Summarization Systems with Dual Augmentation
Xiuying Chen Guodong Long Chongyang Tao Mingzhe Li Xin Gao Chengqi Zhang Xiangliang Zhang
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.