# Document-Level Text Simplification: Dataset, Criteria and Baseline

arXiv: https://arxiv.org/abs/2110.05071 · HTML: https://mlsanity.com/paper/2110.05071/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [Neural CRF Model for Sentence Alignment in Text Simplification](https://mlsanity.com/paper/2005.02324/) (2020)
- [BLEU is Not Suitable for the Evaluation of Text Simplification](https://mlsanity.com/paper/1810.05995/) (2018)
- [EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing](https://mlsanity.com/paper/1906.08104/) (2019)
- [Controllable Text Simplification with Explicit Paraphrasing](https://mlsanity.com/paper/2010.11004/) (2020)
- [BanditSum: Extractive Summarization as a Contextual Bandit](https://mlsanity.com/paper/1809.09672/) (2018)
- [Semantic Structural Evaluation for Text Simplification](https://mlsanity.com/paper/1810.05022/) (2018)
- [Unsupervised Neural Text Simplification](https://mlsanity.com/paper/1810.07931/) (2018)
- [Integrating Transformer and Paraphrase Rules for Sentence Simplification](https://mlsanity.com/paper/1810.11193/) (2018)
- [Dynamic Multi-Level Multi-Task Learning for Sentence Simplification](https://mlsanity.com/paper/1806.07304/) (2018)
- [Split and Rephrase](https://mlsanity.com/paper/1707.06971/) (2017)
- [Simple and Effective Text Simplification Using Semantic and Neural Methods](https://mlsanity.com/paper/1810.05104/) (2018)
- [Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification](https://mlsanity.com/paper/1904.02767/) (2019)
- [Discourse Level Factors for Sentence Deletion in Text Simplification](https://mlsanity.com/paper/1911.10384/) (2019)
- [Sentence Simplification with Memory-Augmented Neural Networks](https://mlsanity.com/paper/1804.07445/) (2018)

## Built on by (most cited first, 27 of 27)

- [OASum: Large-Scale Open Domain Aspect-based Summarization](https://mlsanity.com/paper/2212.09233/) (2022)
- [On the Ethical Considerations of Text Simplification](https://mlsanity.com/paper/2204.09565/) (2022)
- [A Dataset for Plain Language Adaptation of Biomedical Abstracts](https://mlsanity.com/paper/2210.12242/) (2022)
- [Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA](https://mlsanity.com/paper/2305.14458/) (2023)
- [Elaborative Simplification as Implicit Questions Under Discussion](https://mlsanity.com/paper/2305.10387/) (2023)
- [Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification](https://mlsanity.com/paper/2310.08170/) (2023)
- [NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization](https://mlsanity.com/paper/2302.05574/) (2023)
- [SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages](https://mlsanity.com/paper/2305.19204/) (2023)
- [SciNews: From Scholarly Complexities to Public Narratives -- A Dataset for Scientific News Report Generation](https://mlsanity.com/paper/2403.17768/) (2024)
- [Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification](https://mlsanity.com/paper/2305.12463/) (2023)
- [An Open Multilingual System for Scoring Readability of Wikipedia](https://mlsanity.com/paper/2406.01835/) (2024)
- [P-Transformer: Towards Better Document-to-Document Neural Machine Translation](https://mlsanity.com/paper/2212.05830/) (2022)
- [Exploiting Summarization Data to Help Text Simplification](https://mlsanity.com/paper/2302.07124/) (2023)
- [Evaluating Document Simplification: On the Importance of Separately Assessing Simplicity and Meaning Preservation](https://mlsanity.com/paper/2404.03278/) (2024)
- [Progressive Document-level Text Simplification via Large Language Models](https://mlsanity.com/paper/2501.03857/) (2025)
- [Explanatory Summarization with Discourse-Driven Planning](https://mlsanity.com/paper/2504.19339/) (2025)
- [Context-Aware Document Simplification](https://mlsanity.com/paper/2305.06274/) (2023)
- [Is it Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Models](https://mlsanity.com/paper/2309.12551/) (2023)
- [Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding](https://mlsanity.com/paper/2310.11191/) (2023)
- [REFeREE: A REference-FREE Model-Based Metric for Text Simplification](https://mlsanity.com/paper/2403.17640/) (2024)
- [Aligning Sentence Simplification with ESL Learner's Proficiency for Language Acquisition](https://mlsanity.com/paper/2502.11457/) (2025)
- [AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature](https://mlsanity.com/paper/2302.06579/) (2023)
- [Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learning](https://mlsanity.com/paper/2401.15043/) (2024)
- [SimCSum: Joint Learning of Simplification and Cross-lingual Summarization for Cross-lingual Science Journalism](https://mlsanity.com/paper/2304.01621/) (2023)
- [Text Simplification with Sentence Embeddings](https://mlsanity.com/paper/2510.24365/) (2025)
- [Readability Measures and Automatic Text Simplification: In the Search of a Construct](https://mlsanity.com/paper/2511.09536/) (2025)
- [Automatic Simplification of Common Vulnerabilities and Exposures Descriptions](https://mlsanity.com/paper/2602.11982/) (2026)

## 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.