Research Article

Towards Benchmarking Transformer Models for Biomedical Text Simplification

Volume: 9 Number: 1 April 15, 2026

Towards Benchmarking Transformer Models for Biomedical Text Simplification

Abstract

Biomedical texts typically contain a high level of technical terminology and complex sentence structures, which limits their comprehensibility for readers without domain expertise. Text simplification, a natural language processing problem, aims to transform complex texts into a more readable and accessible form while preserving their original semantic content. Especially in biomedical texts, simplification can play an essential role in making scientific information understandable to patients and the general public. In this context, this study investigates the text simplification performance of pre-trained general-purpose and domain-specific language models (PLMs) for biomedical texts. The experiments utilize the Cochrane-Simplification dataset, which comprises technical abstracts from systematic reviews and their corresponding plain language summaries. General-purpose models and summarization tuned variants (BART-Large, BART-Large-CNN, BART-Large-XSum, PEGASUS-Large, PEGASUS-XSum, T5 and FLAN-T5) are compared alongside domain-specific models (BioBARTv2-Large, SciFive, Clinical-T5) under comparable fine-tuning settings. The models were compared using ROUGE, BLEU, BERTScore and SARI metrics to measure textual similarity and semantic coherence. The results indicate that BART based models achieve superior performance in the medical text simplification task.

Keywords

References

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Details

Primary Language

English

Subjects

Natural Language Processing

Journal Section

Research Article

Publication Date

April 15, 2026

Submission Date

February 2, 2026

Acceptance Date

March 11, 2026

Published in Issue

Year 2026 Volume: 9 Number: 1

APA
Mercan, Ö. B., Toçoğlu, M. A., Turhan Turan, N., & Onan, A. (2026). Towards Benchmarking Transformer Models for Biomedical Text Simplification. Scientific Journal of Mehmet Akif Ersoy University, 9(1), 13-29. https://doi.org/10.70030/sjmakeu.1879926
AMA
1.Mercan ÖB, Toçoğlu MA, Turhan Turan N, Onan A. Towards Benchmarking Transformer Models for Biomedical Text Simplification. Techno-Science. 2026;9(1):13-29. doi:10.70030/sjmakeu.1879926
Chicago
Mercan, Öykü Berfin, Mansur Alp Toçoğlu, Nezihe Turhan Turan, and Aytuğ Onan. 2026. “Towards Benchmarking Transformer Models for Biomedical Text Simplification”. Scientific Journal of Mehmet Akif Ersoy University 9 (1): 13-29. https://doi.org/10.70030/sjmakeu.1879926.
EndNote
Mercan ÖB, Toçoğlu MA, Turhan Turan N, Onan A (April 1, 2026) Towards Benchmarking Transformer Models for Biomedical Text Simplification. Scientific Journal of Mehmet Akif Ersoy University 9 1 13–29.
IEEE
[1]Ö. B. Mercan, M. A. Toçoğlu, N. Turhan Turan, and A. Onan, “Towards Benchmarking Transformer Models for Biomedical Text Simplification”, Techno-Science, vol. 9, no. 1, pp. 13–29, Apr. 2026, doi: 10.70030/sjmakeu.1879926.
ISNAD
Mercan, Öykü Berfin - Toçoğlu, Mansur Alp - Turhan Turan, Nezihe - Onan, Aytuğ. “Towards Benchmarking Transformer Models for Biomedical Text Simplification”. Scientific Journal of Mehmet Akif Ersoy University 9/1 (April 1, 2026): 13-29. https://doi.org/10.70030/sjmakeu.1879926.
JAMA
1.Mercan ÖB, Toçoğlu MA, Turhan Turan N, Onan A. Towards Benchmarking Transformer Models for Biomedical Text Simplification. Techno-Science. 2026;9:13–29.
MLA
Mercan, Öykü Berfin, et al. “Towards Benchmarking Transformer Models for Biomedical Text Simplification”. Scientific Journal of Mehmet Akif Ersoy University, vol. 9, no. 1, Apr. 2026, pp. 13-29, doi:10.70030/sjmakeu.1879926.
Vancouver
1.Öykü Berfin Mercan, Mansur Alp Toçoğlu, Nezihe Turhan Turan, Aytuğ Onan. Towards Benchmarking Transformer Models for Biomedical Text Simplification. Techno-Science. 2026 Apr. 1;9(1):13-29. doi:10.70030/sjmakeu.1879926