@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://preview.aclanthology.org/landing_page/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."
}