@inproceedings{gupta-etal-2025-automated,
title = "Automated main concept generation for narrative discourse assessment in aphasia",
author = "Gupta, Ankita and
Hudspeth, Marisa and
Stokes, Polly and
Kurland, Jacquie and
O{'}Connor, Brendan",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1255/",
doi = "10.18653/v1/2025.findings-acl.1255",
pages = "24437--24451",
ISBN = "979-8-89176-256-5",
abstract = "We present an interesting application of narrative understanding in the clinical assessment of aphasia, where story retelling tasks are used to evaluate a patient{'}s communication abilities. This clinical setting provides a framework to help operationalize narrative discourse analysis and an application-focused evaluation method for narrative understanding systems. In particular, we highlight the use of main concepts (MCs){---}a list of statements that capture a story{'}s gist{---}for aphasic discourse analysis. We then propose automatically generating MCs from novel stories, which experts can edit manually, thus enabling wider adaptation of current assessment tools. We further develop a prompt ensemble method using large language models (LLMs) to automatically generate MCs for a novel story. We evaluate our method on an existing narrative summarization dataset to establish its intrinsic validity. We further apply it to a set of stories that have been annotated with MCs through extensive analysis of retells from non-aphasic and aphasic participants (Kurland et al., 2021, 2025). Our results show that our proposed method can generate most of the gold-standard MCs for stories from this dataset. Finally, we release this dataset of stories with annotated MCs to spur more research in this area."
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<abstract>We present an interesting application of narrative understanding in the clinical assessment of aphasia, where story retelling tasks are used to evaluate a patient’s communication abilities. This clinical setting provides a framework to help operationalize narrative discourse analysis and an application-focused evaluation method for narrative understanding systems. In particular, we highlight the use of main concepts (MCs)—a list of statements that capture a story’s gist—for aphasic discourse analysis. We then propose automatically generating MCs from novel stories, which experts can edit manually, thus enabling wider adaptation of current assessment tools. We further develop a prompt ensemble method using large language models (LLMs) to automatically generate MCs for a novel story. We evaluate our method on an existing narrative summarization dataset to establish its intrinsic validity. We further apply it to a set of stories that have been annotated with MCs through extensive analysis of retells from non-aphasic and aphasic participants (Kurland et al., 2021, 2025). Our results show that our proposed method can generate most of the gold-standard MCs for stories from this dataset. Finally, we release this dataset of stories with annotated MCs to spur more research in this area.</abstract>
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%0 Conference Proceedings
%T Automated main concept generation for narrative discourse assessment in aphasia
%A Gupta, Ankita
%A Hudspeth, Marisa
%A Stokes, Polly
%A Kurland, Jacquie
%A O’Connor, Brendan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F gupta-etal-2025-automated
%X We present an interesting application of narrative understanding in the clinical assessment of aphasia, where story retelling tasks are used to evaluate a patient’s communication abilities. This clinical setting provides a framework to help operationalize narrative discourse analysis and an application-focused evaluation method for narrative understanding systems. In particular, we highlight the use of main concepts (MCs)—a list of statements that capture a story’s gist—for aphasic discourse analysis. We then propose automatically generating MCs from novel stories, which experts can edit manually, thus enabling wider adaptation of current assessment tools. We further develop a prompt ensemble method using large language models (LLMs) to automatically generate MCs for a novel story. We evaluate our method on an existing narrative summarization dataset to establish its intrinsic validity. We further apply it to a set of stories that have been annotated with MCs through extensive analysis of retells from non-aphasic and aphasic participants (Kurland et al., 2021, 2025). Our results show that our proposed method can generate most of the gold-standard MCs for stories from this dataset. Finally, we release this dataset of stories with annotated MCs to spur more research in this area.
%R 10.18653/v1/2025.findings-acl.1255
%U https://aclanthology.org/2025.findings-acl.1255/
%U https://doi.org/10.18653/v1/2025.findings-acl.1255
%P 24437-24451
Markdown (Informal)
[Automated main concept generation for narrative discourse assessment in aphasia](https://aclanthology.org/2025.findings-acl.1255/) (Gupta et al., Findings 2025)
ACL