HEAL-Summ: a lightweight and ethical framework for accessible summarization of health information
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces HEAL-Summ, a lightweight framework using large language models (LLMs) to summarize Canadian health news ethically and accessibly. It balances linguistic quality with ethical reliability for public health communication.
Area Of Science
- Natural Language Processing
- Public Health Communication
- Computational Linguistics
Background
- Increasing volume and complexity of health news hinder public understanding.
- Existing large language model (LLM) summarization methods can be computationally expensive and lack ethical evaluation.
- Need for accessible and ethically sound summarization of health information.
Purpose Of The Study
- Propose HEAL-Summ, a lightweight framework for summarizing Canadian health news using LLMs.
- Integrate multiple LLMs (Phi 3, Qwen 2.5, Llama 3.2) within the framework.
- Implement a multi-dimensional evaluation strategy for summarization quality.
Main Methods
- Developed the Health Ethics & Accessibility with Lightweight Summarization (HEAL-Summ) framework.
- Utilized Phi 3, Qwen 2.5, and Llama 3.2 for summarization.
- Applied a comprehensive evaluation including semantic consistency, readability, lexical diversity, emotional alignment, and toxicity.
Main Results
- Consistent semantic agreement observed across all tested LLMs.
- Phi 3 model produced more accessible summaries.
- Qwen 2.5 model demonstrated greater emotional and lexical diversity.
- Statistical significance confirmed differences in readability and emotional tone.
Conclusions
- Lightweight LLMs can facilitate transparent and emotionally sensitive public health communication.
- The HEAL-Summ framework offers a scalable, ethical approach for analyzing health news.
- The framework supports resource-constrained environments needing reliable health information access.
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