Abstract
Text summarization aims to generate a short summary for an input text. In this work, we propose a Non-Autoregressive Unsupervised Summarization (NAUS) approach, which does not require parallel data for training. Our NAUS first performs edit-based search towards a heuristically defined score, and generates a summary as pseudo-groundtruth. Then, we train an encoder-only non-autoregressive Transformer based on the search result. We also propose a dynamic programming approach for length-control decoding, which is important for the summarization task. Experiments on two datasets show that NAUS achieves state-of-the-art performance for unsupervised summarization, yet largely improving inference efficiency. Further, our algorithm is able to perform explicit length-transfer summary generation.- Anthology ID:
- 2022.acl-long.545
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7916–7929
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.545
- DOI:
- 10.18653/v1/2022.acl-long.545
- Cite (ACL):
- Puyuan Liu, Chenyang Huang, and Lili Mou. 2022. Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7916–7929, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization (Liu et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.acl-long.545.pdf
- Code
- manga-uofa/naus