Generating Summaries with Topic Templates and Structured Convolutional Decoders

Laura Perez-Beltrachini, Yang Liu, Mirella Lapata


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P19-1504.pdf
Video:
 https://vimeo.com/385223349
Code
 lauhaide/WikiCatSum
Data
WikiCatSumWikiSum