Summarization Attack via Paraphrasing
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, 2023, 37, pp. 16250-16251
- Issue Date:
- 2023-06-27
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Summarization_Attack_with_Paraphrasing_23 (1).pdf | Accepted version | 127.02 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Many natural language processing models are perceived to be fragile on adversarial attacks. Recent work on adversarial attack has demonstrated a high success rate on sentiment analysis as well as classification models. However, attacks to summarization models have not been well studied. Summarization tasks are rarely influenced by word substitution, since advanced abstractive summary models utilize sentence level information. In this paper, we propose a paraphrasing-based attack method to attack summarization models. We first rank the sentences in the document according to their impacts to summarization. Then, we apply paraphrasing procedure to generate adversarial samples. Finally, we test our algorithm on benchmarks datasets against others methods. Our approach achieved the highest success rate and the lowest sentence substitution rate. In addition, the adversarial samples have high semantic similarity with the original sentences.
Please use this identifier to cite or link to this item: