Abstract
Previous work indicates that discourse information benefits summarization. In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees from pre-trained neural summarizers. In particular, we generate unlabeled RST-style discourse trees from the self-attention matrices of the transformer model. Experiments across models and datasets reveal that the summarizer learns both, dependency- and constituency-style discourse information, which is typically encoded in a single head, covering long- and short-distance discourse dependencies. Overall, the experimental results suggest that the learned discourse information is general and transferable inter-domain.- Anthology ID:
- 2021.naacl-main.326
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4139–4152
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.326
- DOI:
- 10.18653/v1/2021.naacl-main.326
- Cite (ACL):
- Wen Xiao, Patrick Huber, and Giuseppe Carenini. 2021. Predicting Discourse Trees from Transformer-based Neural Summarizers. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4139–4152, Online. Association for Computational Linguistics.
- Cite (Informal):
- Predicting Discourse Trees from Transformer-based Neural Summarizers (Xiao et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2021.naacl-main.326.pdf
- Code
- Wendy-Xiao/summ_guided_disco_parser
- Data
- CNN/Daily Mail, New York Times Annotated Corpus