Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89104
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Title: Retrieve, rerank and rewrite : soft template based neural summarization
Authors: Cao, Z 
Li, W 
Wei, F
Li, S
Issue Date: 2018
Source: In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 15-20 July 2018, (Vol. 1: Long Papers) , p. 152-161. Stroudsburg : Association for Computational Linguistics, 2018.
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.
Publisher: Association for Computational Linguistics (ACL)
DOI: 10.18653/v1/p18-1015
Rights: © 2017 Association for Computational Linguistics
ACL materials are Copyright © 1963–2021 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
The following publication Cao, Z., Li, W., Wei, F., & Li, S. (2018). Retrieve, rerank and rewrite: Soft template based neural summarization. Paper presented at the ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 1, 152-161 is available at https://dx.doi.org/10.18653/v1/p18-1015
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