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    <titleInfo>
        <title>Document-Level Text Simplification: Dataset, Criteria and Baseline</title>
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    <name type="personal">
        <namePart type="given">Renliang</namePart>
        <namePart type="family">Sun</namePart>
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    <name type="personal">
        <namePart type="given">Hanqi</namePart>
        <namePart type="family">Jin</namePart>
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    <name type="personal">
        <namePart type="given">Xiaojun</namePart>
        <namePart type="family">Wan</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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            <namePart type="family">Yih</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Text simplification is a valuable technique. However, current research is limited to sentence simplification. In this paper, we define and investigate a new task of document-level text simplification, which aims to simplify a document consisting of multiple sentences. Based on Wikipedia dumps, we first construct a large-scale dataset named D-Wikipedia and perform analysis and human evaluation on it to show that the dataset is reliable. Then, we propose a new automatic evaluation metric called D-SARI that is more suitable for the document-level simplification task. Finally, we select several representative models as baseline models for this task and perform automatic evaluation and human evaluation. We analyze the results and point out the shortcomings of the baseline models.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.630</identifier>
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        <date>2021-11</date>
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            <start>7997</start>
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%0 Conference Proceedings
%T Document-Level Text Simplification: Dataset, Criteria and Baseline
%A Sun, Renliang
%A Jin, Hanqi
%A Wan, Xiaojun
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F sun-etal-2021-document
%X Text simplification is a valuable technique. However, current research is limited to sentence simplification. In this paper, we define and investigate a new task of document-level text simplification, which aims to simplify a document consisting of multiple sentences. Based on Wikipedia dumps, we first construct a large-scale dataset named D-Wikipedia and perform analysis and human evaluation on it to show that the dataset is reliable. Then, we propose a new automatic evaluation metric called D-SARI that is more suitable for the document-level simplification task. Finally, we select several representative models as baseline models for this task and perform automatic evaluation and human evaluation. We analyze the results and point out the shortcomings of the baseline models.
%R 10.18653/v1/2021.emnlp-main.630
%U https://aclanthology.org/2021.emnlp-main.630
%U https://doi.org/10.18653/v1/2021.emnlp-main.630
%P 7997-8013

Markdown (Informal)

[Document-Level Text Simplification: Dataset, Criteria and Baseline](https://aclanthology.org/2021.emnlp-main.630) (Sun et al., EMNLP 2021)

• Document-Level Text Simplification: Dataset, Criteria and Baseline (Sun et al., EMNLP 2021)

ACL

• Renliang Sun, Hanqi Jin, and Xiaojun Wan. 2021. Document-Level Text Simplification: Dataset, Criteria and Baseline. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7997–8013, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.