Automatic Text Simplification for Lithuanian: Transforming Administrative Texts into Plain Language

Justina Mandravickaitė (), Eglė Rimkienė, Danguolė Kotryna Kapkan, Danguolė Kalinauskaitė, Antanas Čenys () and Tomas Krilavičius
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Justina Mandravickaitė: Faculty of Informatics, Vytautas Magnus University, Kaunas District, 53361 Akademija, Lithuania
Eglė Rimkienė: Faculty of Informatics, Vytautas Magnus University, Kaunas District, 53361 Akademija, Lithuania
Danguolė Kotryna Kapkan: Faculty of Informatics, Vytautas Magnus University, Kaunas District, 53361 Akademija, Lithuania
Danguolė Kalinauskaitė: Faculty of Informatics, Vytautas Magnus University, Kaunas District, 53361 Akademija, Lithuania
Antanas Čenys: Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania
Tomas Krilavičius: Faculty of Informatics, Vytautas Magnus University, Kaunas District, 53361 Akademija, Lithuania

Mathematics, 2025, vol. 13, issue 3, 1-28

Abstract: In this study, we present the results of experiments on text simplification for the Lithuanian language, where we aim to simplify administrative-style texts to the Plain Language level. We selected mT5, mBART, and LT-Llama-2 as the foundational models and fine-tuned them for the text simplification task. Additionally, we evaluated ChatGPT for this purpose. Also, we conducted a comprehensive assessment of the simplification results provided by these models both quantitatively and qualitatively. The results demonstrated that mBART was the most effective model for simplifying Lithuanian administrative text, achieving the highest scores across all the evaluation metrics. A qualitative evaluation of the simplified sentences complemented our quantitative findings. Attention analysis provided insights into model decisions, highlighting strengths in lexical and syntactic simplifications but revealing challenges with longer, complex sentences. Our findings contribute to advancing text simplification for lesser-resourced languages, with practical applications for more effective communication between institutions and the general public, which is the goal of Plain Language.

Keywords: text simplification; Lithuanian; plain language; transformers; mBART; mT5; Llama-2; ChatGPT; fine-tuning; natural language processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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