@inproceedings{nisioi-etal-2017-exploring,
title = "Exploring Neural Text Simplification Models",
author = "Nisioi, Sergiu and
{\v{S}}tajner, Sanja and
Ponzetto, Simone Paolo and
Dinu, Liviu P.",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2014",
doi = "10.18653/v1/P17-2014",
pages = "85--91",
abstract = "We present the first attempt at using sequence to sequence neural networks to model text simplification (TS). Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction. An extensive human evaluation of the output has shown that NTS systems achieve almost perfect grammaticality and meaning preservation of output sentences and higher level of simplification than the state-of-the-art automated TS systems",
}
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%0 Conference Proceedings
%T Exploring Neural Text Simplification Models
%A Nisioi, Sergiu
%A Štajner, Sanja
%A Ponzetto, Simone Paolo
%A Dinu, Liviu P.
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F nisioi-etal-2017-exploring
%X We present the first attempt at using sequence to sequence neural networks to model text simplification (TS). Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction. An extensive human evaluation of the output has shown that NTS systems achieve almost perfect grammaticality and meaning preservation of output sentences and higher level of simplification than the state-of-the-art automated TS systems
%R 10.18653/v1/P17-2014
%U https://aclanthology.org/P17-2014
%U https://doi.org/10.18653/v1/P17-2014
%P 85-91
Markdown (Informal)
[Exploring Neural Text Simplification Models](https://aclanthology.org/P17-2014) (Nisioi et al., ACL 2017)
ACL
- Sergiu Nisioi, Sanja Štajner, Simone Paolo Ponzetto, and Liviu P. Dinu. 2017. Exploring Neural Text Simplification Models. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 85–91, Vancouver, Canada. Association for Computational Linguistics.