mlsanityData to 7 Oct 2026

Document-Level Text Simplification: Dataset, Criteria and Baseline

arXiv 2110.05071 ↗First posted 11 Oct 2021cs.CLcited by 27

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Abstract (arXiv)

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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 18 of its 50 references that are in the map. First posted on arXiv 11 Oct 2021.

Builds on

18 of 50 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2019Text Summarization with Pretrained Encoderscited by 553
  4. 2017Sentence Simplification with Deep Reinforcement Learningcited by 107
  5. 2020Neural CRF Model for Sentence Alignment in Text Simplificationcited by 106
  6. 2018BLEU is Not Suitable for the Evaluation of Text Simplificationcited by 78
  7. 2019EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editingcited by 63
  8. 2020Controllable Text Simplification with Explicit Paraphrasingcited by 53
  9. 2018BanditSum: Extractive Summarization as a Contextual Banditcited by 46
  10. 2018Semantic Structural Evaluation for Text Simplificationcited by 30
  11. 2018Unsupervised Neural Text Simplificationcited by 29
  12. 2018Integrating Transformer and Paraphrase Rules for Sentence Simplificationcited by 29
6 more
  1. 2018Dynamic Multi-Level Multi-Task Learning for Sentence Simplificationcited by 25
  2. 2017Split and Rephrasecited by 24
  3. 2018Simple and Effective Text Simplification Using Semantic and Neural Methodscited by 20
  4. 2019Complexity-Weighted Loss and Diverse Reranking for Sentence Simplificationcited by 20
  5. 2019Discourse Level Factors for Sentence Deletion in Text Simplificationcited by 19
  6. 2018Sentence Simplification with Memory-Augmented Neural Networkscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 12; 2024: 5; 2025: 5; 2026: 1.

27 papers.

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  1. 2022OASum: Large-Scale Open Domain Aspect-based Summarizationcited by 24
  2. 2022On the Ethical Considerations of Text Simplificationcited by 20
  3. 2022A Dataset for Plain Language Adaptation of Biomedical Abstractscited by 19
  4. 2023Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSAcited by 15
  5. 2023Elaborative Simplification as Implicit Questions Under Discussioncited by 14
  6. 2023Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplificationcited by 14
  7. 2023NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarizationcited by 13
  8. 2023SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pagescited by 12
  9. 2024SciNews: From Scholarly Complexities to Public Narratives -- A Dataset for Scientific News Report Generationcited by 12
  10. 2023Teaching the Pre-trained Model to Generate Simple Texts for Text Simplificationcited by 10
  11. 2024An Open Multilingual System for Scoring Readability of Wikipediacited by 7
  12. 2022P-Transformer: Towards Better Document-to-Document Neural Machine Translationcited by 5
15 more
  1. 2023Exploiting Summarization Data to Help Text Simplificationcited by 5
  2. 2024Evaluating Document Simplification: On the Importance of Separately Assessing Simplicity and Meaning Preservationcited by 5
  3. 2025Progressive Document-level Text Simplification via Large Language Modelscited by 5
  4. 2025Explanatory Summarization with Discourse-Driven Planningcited by 4
  5. 2023Context-Aware Document Simplificationcited by 3
  6. 2023Is it Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Modelscited by 3
  7. 2023Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decodingcited by 3
  8. 2024REFeREE: A REference-FREE Model-Based Metric for Text Simplificationcited by 3
  9. 2025Aligning Sentence Simplification with ESL Learner's Proficiency for Language Acquisitioncited by 3
  10. 2023AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literaturecited by 1
  11. 2024Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learningcited by 1
  12. 2023SimCSum: Joint Learning of Simplification and Cross-lingual Summarization for Cross-lingual Science Journalismcited by 0
  13. 2025Text Simplification with Sentence Embeddingscited by 0
  14. 2025Readability Measures and Automatic Text Simplification: In the Search of a Constructcited by 0
  15. 2026Automatic Simplification of Common Vulnerabilities and Exposures Descriptionscited by 0