Papers with simplification models

3 papers
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations (2020.acl-main)

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Challenge: Existing models for sentence simplification are focused on a single transformation, such as lexical paraphrasing or splitting.
Approach: They propose a dataset for assessing sentence simplification in English using a crowdsourced multi-reference corpus.
Outcome: The proposed dataset shows that it captures characteristics of simplicity better than other datasets.
SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages (2023.acl-long)

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Challenge: Prior work on document-level simplification has focused on sentence-level edits, while many desirable edits require document- level context.
Approach: They propose a dataset that reconstructs the document-level editing process from English Wikipedia to paired Simple Wikipedia articles.
Outcome: The proposed dataset reconstructs the document-level editing process from English Wikipedia (EW) articles to paired Simple Wikipedia (SEW) pages.
Evaluating LLMs for Portuguese Sentence Simplification with Linguistic Insights (2025.acl-long)

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Challenge: Sentence simplification (SS) aims to make sentences more straightforward to read and understand without changing its key points.
Approach: They compare 26 state-of-the-art LLMs in Portuguese SS with two simplification models trained explicitly for this task and language.
Outcome: The proposed models outperform open-source models in Portuguese SS . the models are compared against two simplification models trained for Portuguese .

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