HEAL-Summ: a lightweight and ethical framework for accessible summarization of health information.

Introduction: The growing volume and complexity of health-related news presents significant barriers to public understanding. While large language models (LLMs) offer a promising means of summarizing such content, many approaches are computationally expensive and can lack sufficient evaluation of ethical as well as representational quality. Methods: To address these limitations, this research proposes a lightweight framework called Health Ethics & Accessibility with Lightweight Summarization (HEAL-Summ) for summarizing Canadian health news articles using LLMs. The framework incorporates three models (Phi 3, Qwen 2.5, and Llama 3.2) and integrates a multi-dimensional evaluation strategy to assess semantic consistency, readability, lexical diversity, emotional alignment, and toxicity. Results: Comparative analyses shows consistent semantic agreement across models, with Phi yielding more accessible summaries and Qwen producing greater emotional as well as lexical diversity. Statistical significance testing supports key differences in readability and emotional tone. Discussion: This work goes beyond single-model summarization by providing a structured and ethical framework for longitudinal news analysis, emphasizing low-resource deployment and built-in automated evaluations. The findings highlight the potential for lightweight LLMs to facilitate transparent and emotionally sensitive communication in public health, while maintaining a balance between linguistic expressiveness and ethical reliability. The proposed framework offers a scalable path forward for improving access to complex health information in resource-constrained or high-stakes environments.