Self-Repetition in Abstractive Neural Summarizers
Nikita Salkar, Thomas Trikalinos, Byron Wallace, Ani Nenkova
Abstract
We provide a quantitative and qualitative analysis of self-repetition in the output of neural summarizers. We measure self-repetition as the number of n-grams of length four or longer that appear in multiple outputs of the same system. We analyze the behavior of three popular architectures (BART, T5, and Pegasus), fine-tuned on five datasets. In a regression analysis, we find that the three architectures have different propensities for repeating content across output summaries for inputs, with BART being particularly prone to self-repetition. Fine-tuning on more abstractive data, and on data featuring formulaic language is associated with a higher rate of self-repetition. In qualitative analysis, we find systems produce artefacts such as ads and disclaimers unrelated to the content being summarized, as well as formulaic phrases common in the fine-tuning domain. Our approach to corpus-level analysis of self-repetition may help practitioners clean up training data for summarizers and ultimately support methods for minimizing the amount of self-repetition.- Anthology ID:
- 2022.aacl-short.42
- Volume:
- Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2022
- Address:
- Online only
- Venues:
- AACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 341–350
- Language:
- URL:
- https://aclanthology.org/2022.aacl-short.42
- DOI:
- Cite (ACL):
- Nikita Salkar, Thomas Trikalinos, Byron Wallace, and Ani Nenkova. 2022. Self-Repetition in Abstractive Neural Summarizers. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 341–350, Online only. Association for Computational Linguistics.
- Cite (Informal):
- Self-Repetition in Abstractive Neural Summarizers (Salkar et al., AACL-IJCNLP 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.aacl-short.42.pdf