@inproceedings{xiaomeng-etal-2024-wen,
    title = "文本样式和主题框架引导下的大模型辅助儿童新闻生成(Text Styles and Thematic Framework Guided Large Modeling to Aid Children{'}s News Generation)",
    author = "Du, Xiaomeng  and
      Yu, Dong  and
      Liu, Pengyuan",
    editor = "Maosong, Sun  and
      Jiye, Liang  and
      Xianpei, Han  and
      Zhiyuan, Liu  and
      Yulan, He",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/author-degibert/2024.ccl-1.11/",
    pages = "150--170",
    language = "zho",
    abstract = "``主流新闻内容多针对成年人设计,不易于儿童理解,难以满足其阅读需求。对此,我们提出了一种基于主题的儿童新闻篇章结构框架(TNC-LLM)。该框架融合了文本样式定义(TSD)和主题类别定义(TCD)两大核心模块,TSD模块采用多种机器学习算法,从不同粒度分析文本样式风格和段落布局等特点,TCD模块针对不同主题进行了内容分析,以揭示儿童新闻的写作特点和内容的倾向性,确保内容的教育性和适宜性。本文实验主要评估了ChatGPT3.5等四个模型在将成年人新闻转换为面向儿童的新闻的性能。实验结果表明,TNC-LLM在儿童新闻内容生成任务中对内容的准确性、文本的趣味性以及教育性等关键维度有显著提升。此外,该框架具有普适性,能够应用于不同类型的大型语言模型。''"
}