Generating Summaries with Topic Templates and Structured Convolutional Decoders
Abstract
Existing neural generation approaches create multi-sentence text as a single sequence. In this paper we propose a structured convolutional decoder that is guided by the content structure of target summaries. We compare our model with existing sequential decoders on three data sets representing different domains. Automatic and human evaluation demonstrate that our summaries have better content coverage.- Anthology ID:
- P19-1504
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5107–5116
- Language:
- URL:
- https://aclanthology.org/P19-1504
- DOI:
- 10.18653/v1/P19-1504
- Cite (ACL):
- Laura Perez-Beltrachini, Yang Liu, and Mirella Lapata. 2019. Generating Summaries with Topic Templates and Structured Convolutional Decoders. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5107–5116, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Generating Summaries with Topic Templates and Structured Convolutional Decoders (Perez-Beltrachini et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P19-1504.pdf
- Code
- lauhaide/WikiCatSum
- Data
- WikiCatSum, WikiSum