@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 = "Xiaomeng, Du and
Dong, Yu and
Pengyuan, Liu",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
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://aclanthology.org/2024.ccl-1.11/",
pages = "150--170",
language = "zho",
abstract = "{\textquotedblleft}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。{\textquotedblright}"
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xiaomeng-etal-2024-wen">
<titleInfo>
<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)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Du</namePart>
<namePart type="family">Xiaomeng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yu</namePart>
<namePart type="family">Dong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liu</namePart>
<namePart type="family">Pengyuan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<language>
<languageTerm type="text">zho</languageTerm>
</language>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maosong</namePart>
<namePart type="family">Sun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiye</namePart>
<namePart type="family">Liang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xianpei</namePart>
<namePart type="family">Han</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhiyuan</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yulan</namePart>
<namePart type="family">He</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Chinese Information Processing Society of China</publisher>
<place>
<placeTerm type="text">Taiyuan, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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。”</abstract>
<identifier type="citekey">xiaomeng-etal-2024-wen</identifier>
<location>
<url>https://aclanthology.org/2024.ccl-1.11/</url>
</location>
<part>
<date>2024-07</date>
<extent unit="page">
<start>150</start>
<end>170</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T 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)
%A Xiaomeng, Du
%A Dong, Yu
%A Pengyuan, Liu
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F xiaomeng-etal-2024-wen
%X “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。”
%U https://aclanthology.org/2024.ccl-1.11/
%P 150-170
Markdown (Informal)
[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)](https://aclanthology.org/2024.ccl-1.11/) (Xiaomeng et al., CCL 2024)
ACL