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A dataset for plain language adaptation of biomedical abstracts
Though exponentially growing health-related literature has been made available to a broad audience online, the language of scientific articles can be difficult for the general public to understand. Therefore, adapting this expert-level language into plain language versions is necessary for the publi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811873/ https://www.ncbi.nlm.nih.gov/pubmed/36599892 http://dx.doi.org/10.1038/s41597-022-01920-3 |
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author | Attal, Kush Ondov, Brian Demner-Fushman, Dina |
author_facet | Attal, Kush Ondov, Brian Demner-Fushman, Dina |
author_sort | Attal, Kush |
collection | PubMed |
description | Though exponentially growing health-related literature has been made available to a broad audience online, the language of scientific articles can be difficult for the general public to understand. Therefore, adapting this expert-level language into plain language versions is necessary for the public to reliably comprehend the vast health-related literature. Deep Learning algorithms for automatic adaptation are a possible solution; however, gold standard datasets are needed for proper evaluation. Proposed datasets thus far consist of either pairs of comparable professional- and general public-facing documents or pairs of semantically similar sentences mined from such documents. This leads to a trade-off between imperfect alignments and small test sets. To address this issue, we created the Plain Language Adaptation of Biomedical Abstracts dataset. This dataset is the first manually adapted dataset that is both document- and sentence-aligned. The dataset contains 750 adapted abstracts, totaling 7643 sentence pairs. Along with describing the dataset, we benchmark automatic adaptation on the dataset with state-of-the-art Deep Learning approaches, setting baselines for future research. |
format | Online Article Text |
id | pubmed-9811873 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98118732023-01-04 A dataset for plain language adaptation of biomedical abstracts Attal, Kush Ondov, Brian Demner-Fushman, Dina Sci Data Data Descriptor Though exponentially growing health-related literature has been made available to a broad audience online, the language of scientific articles can be difficult for the general public to understand. Therefore, adapting this expert-level language into plain language versions is necessary for the public to reliably comprehend the vast health-related literature. Deep Learning algorithms for automatic adaptation are a possible solution; however, gold standard datasets are needed for proper evaluation. Proposed datasets thus far consist of either pairs of comparable professional- and general public-facing documents or pairs of semantically similar sentences mined from such documents. This leads to a trade-off between imperfect alignments and small test sets. To address this issue, we created the Plain Language Adaptation of Biomedical Abstracts dataset. This dataset is the first manually adapted dataset that is both document- and sentence-aligned. The dataset contains 750 adapted abstracts, totaling 7643 sentence pairs. Along with describing the dataset, we benchmark automatic adaptation on the dataset with state-of-the-art Deep Learning approaches, setting baselines for future research. Nature Publishing Group UK 2023-01-04 /pmc/articles/PMC9811873/ /pubmed/36599892 http://dx.doi.org/10.1038/s41597-022-01920-3 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Attal, Kush Ondov, Brian Demner-Fushman, Dina A dataset for plain language adaptation of biomedical abstracts |
title | A dataset for plain language adaptation of biomedical abstracts |
title_full | A dataset for plain language adaptation of biomedical abstracts |
title_fullStr | A dataset for plain language adaptation of biomedical abstracts |
title_full_unstemmed | A dataset for plain language adaptation of biomedical abstracts |
title_short | A dataset for plain language adaptation of biomedical abstracts |
title_sort | dataset for plain language adaptation of biomedical abstracts |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811873/ https://www.ncbi.nlm.nih.gov/pubmed/36599892 http://dx.doi.org/10.1038/s41597-022-01920-3 |
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