# Faithful Summarization of Consumer Health Queries: A Cross-Lingual Framework with LLMs

arXiv: https://arxiv.org/abs/2511.10768 · HTML: https://mlsanity.com/paper/2511.10768/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [Assessing Large Language Models for Medical QA: Zero-Shot and LLM-as-a-Judge Evaluation](https://mlsanity.com/paper/2602.14564/) (2026)

## Abstract (arXiv)

Summarizing consumer health questions (CHQs) can ease communication in healthcare, but unfaithful summaries that misrepresent medical details pose serious risks. We propose a framework that combines TextRank-based sentence extraction and medical named entity recognition with large language models (LLMs) to enhance faithfulness in medical text summarization. In our experiments, we fine-tuned the LLaMA-2-7B model on the MeQSum (English) and BanglaCHQ-Summ (Bangla) datasets, achieving consistent improvements across quality (ROUGE, BERTScore, readability) and faithfulness (SummaC, AlignScore) metrics, and outperforming zero-shot baselines and prior systems. Human evaluation further shows that over 80% of generated summaries preserve critical medical information. These results highlight faithfulness as an essential dimension for reliable medical summarization and demonstrate the potential of our approach for safer deployment of LLMs in healthcare contexts.