ArticleScientific data2023
A dataset for plain language adaptation of biomedical abstracts.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Lessons from the TREC Plain Language Adaptation of Biomedical Abstracts (PLABA) track.Journal of biomedical informatics · 2026Article
- PatientEase-Domain-Aware RAG for Rehabilitation Instruction Simplification.Bioengineering (Basel, Switzerland) · 2025Article
- A Generative Expert-Narrated Simplification Model for Enhancing Health Literacy Among the Older Population.Bioengineering (Basel, Switzerland) · 2025Article
- Ontology enrichment using a large language model: Applying lexical, semantic, and knowledge network-based similarity for concept placement.Journal of biomedical informatics · 2025Article
- Article
- A Dataset of Medical Questions Paired with Automatically Generated Answers and Evidence-supported References.Scientific data · 2025Article
- APPLS: Evaluating Evaluation Metrics for Plain Language Summarization.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2024Article
- README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Harnessing large language models' zero-shot and few-shot learning capabilities for regulatory research.Briefings in bioinformatics · 2024Article
- Sentence-Aligned Simplification of Biomedical Abstracts.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2024Article
- Improving Biomedical Science Literacy and Patient-Directed Knowledge of Tuberculosis (TB): A Cross-Sectional Infodemiology Study Examining Readability of Patient-Facing TB Information.British journal of biomedical science · 2024Article
- Retrieval augmentation of large language models for lay language generation.Journal of biomedical informatics · 2024Article
- A dataset for plain language adaptation of biomedical abstracts.Scientific data · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.