@inproceedings{zhang-etal-2025-rehearse,
    title = "Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models",
    author = "Zhang, Yanyue  and
      He, Yulan  and
      Zhou, Deyu",
    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/transition-to-people-yaml/2025.findings-acl.787/",
    doi = "10.18653/v1/2025.findings-acl.787",
    pages = "15194--15211",
    ISBN = "979-8-89176-256-5",
    abstract = "Personalized opinion summarization is crucial as it considers individual user interests while generating product summaries.Recent studies show that although large language models demonstrate powerful text summarization and evaluation capabilities without the need for training data, they face difficulties in personalized tasks involving long texts. To address this, \textbf{Rehearsal}, a personalized opinion summarization framework via LLM-based role-playing is proposed. Having the model act as the user, the model can better understand the user{'}s personalized needs.Additionally, a role-playing supervisor and practice process are introduced to improve the role-playing ability of the LLMs, leading to a better expression of user needs.Furthermore, the summary generation process is guided by suggestions from virtual users, ensuring that the generated summary includes the user{'}s interest, thus achieving personalized summary generation. Experiment results demonstrate that our method can effectively improve the level of personalization in large model-generated summaries."
}