  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.
