Language technologies increasingly mediate access to scientific information, yet their impact depends on whether they improve real reading experiences for diverse audiences. We study large language model (LLM)-generated scientific summaries as an accessibility intervention for neurodivergent adults, focusing on readers with ADHD and/or autism. Using a mixed-methods design, we first conducted a formative qualitative study with 10 neurodivergent adults to identify design needs. We then ran a quantitative experiment with 200 neurodivergent adults comparing five summary styles across lower- and higher-complexity scientific texts, measuring comprehension, trust, engagement, and cognitive load. Results show that writing-style effects depend on text complexity: structured LLM-generated summaries improved comprehension for higher-complexity articles but reduced comprehension for lower-complexity articles relative to a baseline human-generated extractive summary. Trust increased for simpler LLM summary styles, while engagement and cognitive load showed fewer robust writing-style effects. Our findings suggest that accessible NLP systems should adapt explanatory support to reader needs and text difficulty, rather than offering a single “best” summary style.