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.

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