Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text
Philippe Laban, Tobias Schnabel, Paul Bennett, Marti A. Hearst
Abstract
This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. We train the model with a novel algorithm to optimize the reward (k-SCST), in which the model proposes several candidate simplifications, computes each candidate’s reward, and encourages candidates that outperform the mean reward. Finally, we propose a realistic text comprehension task as an evaluation method for text simplification. When tested on the English news domain, the KiS model outperforms strong supervised baselines by more than 4 SARI points, and can help people complete a comprehension task an average of 18% faster while retaining accuracy, when compared to the original text.- Anthology ID:
- 2021.acl-long.498
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6365–6378
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.498
- DOI:
- 10.18653/v1/2021.acl-long.498
- Cite (ACL):
- Philippe Laban, Tobias Schnabel, Paul Bennett, and Marti A. Hearst. 2021. Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6365–6378, Online. Association for Computational Linguistics.
- Cite (Informal):
- Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text (Laban et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.498.pdf
- Code
- tingofurro/keep_it_simple
- Data
- Newsela, WikiLarge