Text Simplification Research: A Systematic Literature Review on Languages, Datasets, and Modeling Trends

Lisnawita; Juhaida Abu Bakar; Ruziana Mohamad Rasli

doi:10.15575/join.v11i2.1792

Authors

Lisnawita Faculty of Computer Science, Universitas Lancang Kuning, Indonesia
Juhaida Abu Bakar Data Science Research Lab, School of Computing, Universiti Utara Malaysia, Malaysia
Ruziana Mohamad Rasli School of Multimedia Technology and Communication, Universiti Utara Malaysia, Malaysia

DOI:

https://doi.org/10.15575/join.v11i2.1792

Keywords:

Lexical Simplification, Low-Resource Language, Readability , Systematic Literature Review, Text Simplification, Transformer

Abstract

Text simplification (TS) is a Natural Language Processing (NLP) task that reduces linguistic complexity while preserving meaning and supports accessibility for different reader groups (e.g., children, second-language learners, and readers with cognitive or reading difficulties). TS studies are diverse in languages, datasets, and methods. Therefore, a consolidated overview is needed to summarize trends and guide future research. This study conducts a Systematic Literature Review (SLR) of TS research published from 2015 to 31 July 2025 to map trends in languages, target audiences, datasets/corpora, techniques, models/algorithms, and frameworks. Relevant literature was searched using IEEE Xplore, ScienceDirect (Elsevier), SpringerLink, and Google Scholar, applied predefined inclusion–exclusion criteria, and screened studies using a PRISMA-style process. The final set includes 75 primary studies. The results show that English dominates TS research, commonly used resources include Wikipedia-family datasets and Newsela, and modeling trends shift from rule-based and classical machine learning toward transformer-based architectures (e.g., BERT and Seq2Seq) and large language models (LLMs). This review offers: (1) a structured overview of TS research dimensions, (2) an evidence-based summary of key resources and modeling trends, and (3) recommendations for future research, particularly for low-resource languages and audience-specific evaluation.

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