@inproceedings{zhao-etal-2025-simplified,
    title = "Simplified Rewriting Improves Expert Summarization",
    author = "Zhao, Xingmeng  and
      Wang, Tongnian  and
      Rios, Anthony",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.165/",
    pages = "3075--3097",
    ISBN = "979-8-89176-298-5",
    abstract = "Radiology report summarization (RRS) is critical for clinical workflows, requiring concise Impressions ``distilled from detailed Findings.'' This paper proposes a novel prompting strategy that enhances RRS by introducing a layperson summary as an intermediate step. This summary helps normalize key observations and simplify complex terminology using communication techniques inspired by doctor{--}patient interactions. Combined with few-shot in-context learning, this approach improves the model{'}s ability to map generalized descriptions to specific clinical findings. We evaluate our method on three benchmark datasets, MIMIC-CXR, CheXpert, and MIMIC-III, and compare it against state-of-the-art open-source language models in the 7B/8B parameter range, such as Llama-3.1-8B-Instruct. Results show consistent improvements in summarization quality, with gains of up to 5{\%} on some metrics for prompting, and more than 20{\%} for some models when instruction tuning."
}