We introduce the new task of clinically meaningful summarisation of social media user timelines, appropriate for mental health monitoring. We develop a novel approach for unsupervised abstractive summarisation that produces a two-layer summary consisting of both high-level information, covering aspects useful to clinical experts, as well as accompanying time sensitive evidence from a user's social media timeline. A key methodological novelty comes from the timeline summarisation component based on a version of hierarchical variational autoencoder (VAE) adapted to represent long texts and guided by LLM-annotated key phrases. The resulting timeline summary is input into a LLM (LLaMA-2) to produce the final summary containing both the high level information, obtained through instruction prompting, as well as corresponding evidence from the user's timeline. We assess the summaries generated by our novel architecture via automatic evaluation against expert written summaries and via human evaluation with clinical experts, showing that timeline summarisation by TH-VAE results in logically coherent summaries rich in clinical utility and superior to LLM-only approaches in capturing changes over time.

我们引入了临床意义的社交媒体用户时间线总结的新任务，适用于精神健康监测。我们开发了一种新颖的无监督抽象总结方法，生成由高层次信息和用户社交媒体时间线中与时间敏感证据相配套的双层总结。我们的方法的关键方法创新来自基于一种适应表示长文本的分层变分自动编码器（VAE）版本和由LLM注释的关键短语引导的时间线总结组件。通过自动评估和临床专家的人工评估，我们评估了我们的新型架构生成的总结，结果显示TH-VAE进行的时间线总结逻辑连贯，临床效用丰富，并且在捕捉时间变化方面优于仅使用LLM方法。

社交媒体中具临床意义的心理健康监测时间线总结