<![CDATA[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.  ]]>
Copyrights © 2026
