Impact of Text Simplification on Eye-Tracking-Based Reading Profiles Across Domains
Proceedings fo the Second International Workshop on Eye-Tracking Resources and Evaluation for Human-Aligned NLP
Abstract
Understanding how text readability affects reading behaviour is crucial for improving accessibility and health communication. We analyse sentence-level eye-tracking data from the French Eye-TrAcking (FETA) corpus, which includes original and manually simplified texts from three domains: general, medical, and clinical. Using clustering of fixation-based features, we identify recurrent processing patterns and examine how these patterns change under text simplification. Cluster quality is evaluated using silhouette scores and participant-level bootstrap stability. Simplification does not uniformly reduce reading effort but reorganises processing in domain-dependent ways. Medical texts show strong diversification, general texts moderate diversification, and clinical texts show a reduction in the number of distinct reading profiles. Hence, rather than uniformly facilitating reading, simplification redistributes effort across sentences, underscoring the need for domain-sensitive readability approaches.