Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization

Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei


Abstract
Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably. Inspired by the traditional template-based summarization approaches, this paper proposes to use existing summaries as soft templates to guide the seq2seq model. To this end, we use a popular IR platform to Retrieve proper summaries as candidate templates. Then, we extend the seq2seq framework to jointly conduct template Reranking and template-aware summary generation (Rewriting). Experiments show that, in terms of informativeness, our model significantly outperforms the state-of-the-art methods, and even soft templates themselves demonstrate high competitiveness. In addition, the import of high-quality external summaries improves the stability and readability of generated summaries.
Anthology ID:
P18-1015
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
152–161
Language:
URL:
https://aclanthology.org/P18-1015
DOI:
10.18653/v1/P18-1015
Bibkey:
Cite (ACL):
Ziqiang Cao, Wenjie Li, Sujian Li, and Furu Wei. 2018. Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 152–161, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization (Cao et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1015.pdf
Presentation:
 P18-1015.Presentation.pdf
Video:
 https://vimeo.com/285800584