We introduce a hybrid abstractive summarisation approach combining hierarchical VAE with LLMs (LlaMA-2) to produce clinically meaningful summaries from social media user timelines, appropriate for mental health monitoring. The summaries combine two different narrative points of view: clinical insights in third person useful for a clinician are generated by feeding into an LLM specialised clinical prompts, and importantly, a temporally sensitive abstractive summary of the user's timeline in first person, generated by a novel hierarchical variational autoencoder, TH-VAE. We assess the generated summaries via automatic evaluation against expert summaries and via human evaluation with clinical experts, showing that timeline summarisation by TH-VAE results in more factual and logically coherent summaries rich in clinical utility and superior to LLM-only approaches in capturing changes over time.

翻译：我们提出了一种混合式抽象摘要方法，将层次化变分自编码器（hierarchical VAE）与大语言模型（LLaMA-2）相结合，从社交媒体用户时间线中生成具有临床意义的摘要，适用于心理健康监测。该摘要融合了两种不同的叙事视角：一是通过向LLM输入专门的临床提示生成的第三人称临床见解，供临床医师使用；二是通过新型层次化变分自编码器TH-VAE生成的用户时间线第一人称时间敏感型抽象摘要。我们通过自动评估与专家摘要的对比，以及临床专家的人工评估，验证了所生成摘要的效果。结果表明，TH-VAE生成的时间线摘要具有更高的事实准确性和逻辑连贯性，富含临床实用价值，并且在捕捉随时间变化方面优于仅使用大语言模型的方法。