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        <title>On the Helpfulness of Document Context to Sentence Simplification</title>
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    <name type="personal">
        <namePart type="given">Renliang</namePart>
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    <name type="personal">
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            <namePart type="given">Chengqing</namePart>
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    <abstract>Most of the research on text simplification is limited to sentence level nowadays. In this paper, we are the first to investigate the helpfulness of document context on sentence simplification and apply it to the sequence-to-sequence model. We firstly construct a sentence simplification dataset in which the contexts for the original sentence are provided by Wikipedia corpus. The new dataset contains approximately 116K sentence pairs with context. We then propose a new model that makes full use of the context information. Our model uses neural networks to learn the different effects of the preceding sentences and the following sentences on the current sentence and applies them to the improved transformer model. Evaluated on the newly constructed dataset, our model achieves 36.52 on SARI value, which outperforms the best performing model in the baselines by 2.46 (7.22%), indicating that context indeed helps improve sentence simplification. In the ablation experiment, we show that using either the preceding sentences or the following sentences as context can significantly improve simplification.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.121</identifier>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T On the Helpfulness of Document Context to Sentence Simplification
%A Sun, Renliang
%A Lin, Zhe
%A Wan, Xiaojun
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F sun-etal-2020-helpfulness
%X Most of the research on text simplification is limited to sentence level nowadays. In this paper, we are the first to investigate the helpfulness of document context on sentence simplification and apply it to the sequence-to-sequence model. We firstly construct a sentence simplification dataset in which the contexts for the original sentence are provided by Wikipedia corpus. The new dataset contains approximately 116K sentence pairs with context. We then propose a new model that makes full use of the context information. Our model uses neural networks to learn the different effects of the preceding sentences and the following sentences on the current sentence and applies them to the improved transformer model. Evaluated on the newly constructed dataset, our model achieves 36.52 on SARI value, which outperforms the best performing model in the baselines by 2.46 (7.22%), indicating that context indeed helps improve sentence simplification. In the ablation experiment, we show that using either the preceding sentences or the following sentences as context can significantly improve simplification.
%R 10.18653/v1/2020.coling-main.121
%U https://aclanthology.org/2020.coling-main.121/
%U https://doi.org/10.18653/v1/2020.coling-main.121
%P 1411-1423

Markdown (Informal)

[On the Helpfulness of Document Context to Sentence Simplification](https://aclanthology.org/2020.coling-main.121/) (Sun et al., COLING 2020)

• On the Helpfulness of Document Context to Sentence Simplification (Sun et al., COLING 2020)

ACL

• Renliang Sun, Zhe Lin, and Xiaojun Wan. 2020. On the Helpfulness of Document Context to Sentence Simplification. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1411–1423, Barcelona, Spain (Online). International Committee on Computational Linguistics.