TY  - EJOU
AU  - Käser, Josua 
AU  - Nagy, Thomas 
AU  - Stirnemann, Patrick 
AU  - Hanne, Thomas 

TI  - Multilingual Text Summarization in Healthcare Using Pre-Trained Transformer-Based Language Models
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 1
SN  - 1546-2226

AB  - We analyze the suitability of existing pre-trained transformer-based language models (PLMs) for abstractive text summarization on German technical healthcare texts. The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field. The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts, even if the model is not specifically trained in that language. Through experiments, the research questions explore the performance of transformer language models in dealing with complex syntax constructs, the difference in performance between models trained in English and German, and the impact of translating the source text to English before conducting the summarization. We conducted an evaluation of four PLMs (GPT-3, a translation-based approach also utilizing GPT-3, a German language Model, and a domain-specific bio-medical model approach). The evaluation considered the informativeness using 3 types of metrics based on Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and the quality of results which is manually evaluated considering 5 aspects. The results show that text summarization models could be used in the German healthcare domain and that domain-independent language models achieved the best results. The study proves that text summarization models can simplify the search for pre-existing German knowledge in various domains.
KW  - Text summarization; pre-trained transformer-based language models; large language models; technical healthcare texts; natural language processing

DO  - 10.32604/cmc.2025.061527