Abstract
We address the problem of unsupervised abstractive summarization of collections of user generated reviews through self-supervision and control. We propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. This setting makes training simpler than previous approaches by relying only on standard log-likelihood loss and mainstream models. We address the problem of hallucinations through the use of control codes, to steer the generation towards more coherent and relevant summaries.- Anthology ID:
- 2021.eacl-main.141
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1646–1662
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.141
- DOI:
- 10.18653/v1/2021.eacl-main.141
- Cite (ACL):
- Hady Elsahar, Maximin Coavoux, Jos Rozen, and Matthias Gallé. 2021. Self-Supervised and Controlled Multi-Document Opinion Summarization. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1646–1662, Online. Association for Computational Linguistics.
- Cite (Informal):
- Self-Supervised and Controlled Multi-Document Opinion Summarization (Elsahar et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.eacl-main.141.pdf