@inproceedings{bian-etal-2026-plaba,
title = "{PLABA}-{EVAL}: A Multi-Dimensional, In-Context Sentence Readability Dataset for Medical Text",
author = "Bian, Kexin and
Yoon, Su-Youn and
Komachi, Mamoru",
editor = "Shardlow, Matthew and
Fran{\c{c}}ois, Thomas and
Amaro, Raquel and
Baptista, Jorge and
Cardon, R{\'e}mi and
Ribeiro, Eug{\'e}nio and
Saggion, Horacio and
Stodden, Regina and
Todirascu, Amalia and
Wilkens, Rodrigo",
booktitle = "Proceedings of the Joint Workshop on Readability and Text Simplification ({READI}x{TSAR}) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.readi-1.4/",
doi = "10.63317/2y6bctu9etkj",
pages = "49--60",
abstract = "We present an in-context framework for assessing readability that separates reading difficulty into multiple subjective dimensions. Participants read biomedical abstracts with full-document access and provide sentence-level ratings of Processing Ease and Perceived Understanding, followed by an open-book multiple-choice comprehension check. Using this protocol, we release PLABA-EVAL, a dataset of 78 biomedical abstracts and expert plain-language adaptations (609 sentences), annotated by three independent raters per document. Analyses show that Ease and Understanding are strongly related but not interchangeable, and that perceived understanding aligns more closely with open-book comprehension performance. We provide baseline linguistic analyses for both dimensions, illustrating how the dataset supports work on readability, simplification, and sentence-level difficulty modeling."
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<abstract>We present an in-context framework for assessing readability that separates reading difficulty into multiple subjective dimensions. Participants read biomedical abstracts with full-document access and provide sentence-level ratings of Processing Ease and Perceived Understanding, followed by an open-book multiple-choice comprehension check. Using this protocol, we release PLABA-EVAL, a dataset of 78 biomedical abstracts and expert plain-language adaptations (609 sentences), annotated by three independent raters per document. Analyses show that Ease and Understanding are strongly related but not interchangeable, and that perceived understanding aligns more closely with open-book comprehension performance. We provide baseline linguistic analyses for both dimensions, illustrating how the dataset supports work on readability, simplification, and sentence-level difficulty modeling.</abstract>
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%0 Conference Proceedings
%T PLABA-EVAL: A Multi-Dimensional, In-Context Sentence Readability Dataset for Medical Text
%A Bian, Kexin
%A Yoon, Su-Youn
%A Komachi, Mamoru
%Y Shardlow, Matthew
%Y François, Thomas
%Y Amaro, Raquel
%Y Baptista, Jorge
%Y Cardon, Rémi
%Y Ribeiro, Eugénio
%Y Saggion, Horacio
%Y Stodden, Regina
%Y Todirascu, Amalia
%Y Wilkens, Rodrigo
%S Proceedings of the Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F bian-etal-2026-plaba
%X We present an in-context framework for assessing readability that separates reading difficulty into multiple subjective dimensions. Participants read biomedical abstracts with full-document access and provide sentence-level ratings of Processing Ease and Perceived Understanding, followed by an open-book multiple-choice comprehension check. Using this protocol, we release PLABA-EVAL, a dataset of 78 biomedical abstracts and expert plain-language adaptations (609 sentences), annotated by three independent raters per document. Analyses show that Ease and Understanding are strongly related but not interchangeable, and that perceived understanding aligns more closely with open-book comprehension performance. We provide baseline linguistic analyses for both dimensions, illustrating how the dataset supports work on readability, simplification, and sentence-level difficulty modeling.
%R 10.63317/2y6bctu9etkj
%U https://aclanthology.org/2026.readi-1.4/
%U https://doi.org/10.63317/2y6bctu9etkj
%P 49-60
Markdown (Informal)
[PLABA-EVAL: A Multi-Dimensional, In-Context Sentence Readability Dataset for Medical Text](https://aclanthology.org/2026.readi-1.4/) (Bian et al., READI-TSAR 2026)
ACL