# A Dataset for Plain Language Adaptation of Biomedical Abstracts

arXiv: https://arxiv.org/abs/2210.12242 · HTML: https://mlsanity.com/paper/2210.12242/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [Neural CRF Model for Sentence Alignment in Text Simplification](https://mlsanity.com/paper/2005.02324/) (2020)
- [Neural Machine Translation: A Review and Survey](https://mlsanity.com/paper/1912.02047/) (2019)
- [Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen](https://mlsanity.com/paper/2005.00701/) (2020)
- [Question-Driven Summarization of Answers to Consumer Health Questions](https://mlsanity.com/paper/2005.09067/) (2020)
- [Document-Level Text Simplification: Dataset, Criteria and Baseline](https://mlsanity.com/paper/2110.05071/) (2021)
- [AutoMeTS: The Autocomplete for Medical Text Simplification](https://mlsanity.com/paper/2010.10573/) (2020)

## Built on by (most cited first, 19 of 19)

- [Retrieval augmentation of large language models for lay language generation](https://mlsanity.com/paper/2211.03818/) (2022)
- [README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP](https://mlsanity.com/paper/2312.15561/) (2023)
- [APPLS: Evaluating Evaluation Metrics for Plain Language Summarization](https://mlsanity.com/paper/2305.14341/) (2023)
- [Investigating Large Language Models and Control Mechanisms to Improve Text Readability of Biomedical Abstracts](https://mlsanity.com/paper/2309.13202/) (2023)
- [Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation](https://mlsanity.com/paper/2508.08730/) (2025)
- [ConSens: Assessing context grounding in open-book question answering](https://mlsanity.com/paper/2505.00065/) (2025)
- [Sci-LoRA: Mixture of Scientific LoRAs for Cross-Domain Lay Paraphrasing](https://mlsanity.com/paper/2505.18867/) (2025)
- [BoschAI @ PLABA 2023: Leveraging Edit Operations in End-to-End Neural Sentence Simplification](https://mlsanity.com/paper/2311.01907/) (2023)
- [Large Language Models for Biomedical Text Simplification: Promising But Not There Yet](https://mlsanity.com/paper/2408.03871/) (2024)
- [MaLei at the PLABA Track of TREC 2024: RoBERTa for Term Replacement -- LLaMA3.1 and GPT-4o for Complete Abstract Adaptation](https://mlsanity.com/paper/2411.07381/) (2024)
- [PERCS: Persona-Guided Controllable Biomedical Summarization Dataset](https://mlsanity.com/paper/2512.03340/) (2025)
- [Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoning](https://mlsanity.com/paper/2603.28325/) (2026)
- [UM_FHS at TREC 2024 PLABA: Exploration of Fine-tuning and AI agent approach for plain language adaptations of biomedical text](https://mlsanity.com/paper/2502.14144/) (2025)
- [Am I eligible? Natural Language Inference for Clinical Trial Patient Recruitment: the Patient's Point of View](https://mlsanity.com/paper/2503.15718/) (2025)
- [JEBS: A Fine-grained Biomedical Lexical Simplification Task](https://mlsanity.com/paper/2506.12898/) (2025)
- [Making Knowledge Accessible: Divergent Readability-Accuracy Strategies of Mistral and QWen in Biomedical Text Simplification](https://mlsanity.com/paper/2511.05080/) (2025)
- [HealthcareNLP: where are we and what is next?](https://mlsanity.com/paper/2512.08617/) (2025)
- [Plain language adaptations of biomedical text using LLMs: Comparision of evaluation metrics](https://mlsanity.com/paper/2512.16530/) (2025)
- [Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation](https://mlsanity.com/paper/2609.17398/) (2026)

## Abstract (arXiv)

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.