THM@SimpleText 2025 -- Task 1.1: Revisiting Text Simplification based on Complex Terms for Non-Experts
This study addresses the challenge of low comprehensibility of context-poor, ultra-short scientific sentences—such as abstracts and titles—for non-expert readers. We propose a term-level simplification method that first precisely identifies domain-specific complex terms within the input sentence, then employs coordinated lightweight Gemini and OpenAI models for controlled, semantics-preserving rephrasing—avoiding global rewriting to prevent information distortion. Our approach integrates rule-based terminology detection with large language models’ semantic fidelity in generation. Evaluated on CLEF SimpleText 2025 Task 1.1, it significantly improves readability and accuracy for low-context scientific sentences. Experiments demonstrate superiority over baselines across three dimensions: plausibility of term replacement, preservation of original meaning, and comprehension by non-experts. The method thus enhances public accessibility of scientific knowledge without compromising technical integrity.