Clinically meaningful timeline summarisation in social media for mental health monitoring
CoRR(2024)
Abstract
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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