@inproceedings{song-etal-2024-combining,
title = "Combining Hierachical {VAE}s with {LLM}s for clinically meaningful timeline summarisation in social media",
author = "Song, Jiayu and
Chim, Jenny and
Tsakalidis, Adam and
Ive, Julia and
Atzil-Slonim, Dana and
Liakata, Maria",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.873",
doi = "10.18653/v1/2024.findings-acl.873",
pages = "14651--14672",
abstract = "We introduce a hybrid abstractive summarisation approach combining hierarchical VAEs with LLMs to produce clinically meaningful summaries from social media user timelines, appropriate for mental health monitoring. The summaries combine two different narrative points of view: (a) clinical insights in third person, generated by feeding into an LLM clinical expert-guided prompts, and importantly, (b) 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.",
}
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<abstract>We introduce a hybrid abstractive summarisation approach combining hierarchical VAEs with LLMs to produce clinically meaningful summaries from social media user timelines, appropriate for mental health monitoring. The summaries combine two different narrative points of view: (a) clinical insights in third person, generated by feeding into an LLM clinical expert-guided prompts, and importantly, (b) 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.</abstract>
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%0 Conference Proceedings
%T Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media
%A Song, Jiayu
%A Chim, Jenny
%A Tsakalidis, Adam
%A Ive, Julia
%A Atzil-Slonim, Dana
%A Liakata, Maria
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F song-etal-2024-combining
%X We introduce a hybrid abstractive summarisation approach combining hierarchical VAEs with LLMs to produce clinically meaningful summaries from social media user timelines, appropriate for mental health monitoring. The summaries combine two different narrative points of view: (a) clinical insights in third person, generated by feeding into an LLM clinical expert-guided prompts, and importantly, (b) 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.
%R 10.18653/v1/2024.findings-acl.873
%U https://aclanthology.org/2024.findings-acl.873
%U https://doi.org/10.18653/v1/2024.findings-acl.873
%P 14651-14672
Markdown (Informal)
[Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media](https://aclanthology.org/2024.findings-acl.873) (Song et al., Findings 2024)
ACL