Divergent strategies in text simplification: A comparative analysis of AI and human approaches in language processing

Abstract This study explores the growing importance of text simplification, particularly in light of increasing global migration and the demand for accessible information. We compare AI-generated and human-produced simplifications of Japanese news articles, focusing on whether GPT-4 can outperform humans in simplification tasks and how their strategies differ. Analyzing 420 original articles and their simplified versions created by human experts and ChatGPT (GPT-4), we employed a readability formula specifically designed for Japanese texts and conducted text feature analysis. Our findings reveal that AI-simplified texts achieve significantly higher readability scores than human-simplified versions while retaining more original content. Specifically, AI-simplified texts exhibit shorter average sentence length and lower complex word usage. We argue that AI, leveraging transformer architectures with self-attention mechanisms, can perform multidimensional simplification more effectively than humans, potentially due to its ability to process information beyond sentence boundaries during training. Furthermore, this study highlights the cognitive challenges humans face in text simplification, offering insights into human language processing, including its limitations and creative potential. Thus, our research confirms the potential of AI in language processing while simultaneously providing a perspective for understanding human cognitive processes in language use. The findings have implications for language education, linguistic research, and the development of accessible information dissemination methods, particularly for initiatives like “Easy Japanese.” We also suggest future research directions, including exploring human-AI collaboration in text simplification.