摘要
【目的】基于主题聚类生成新媒体政务互动内容舆情摘要,保障政府部门对舆情事件的及时准确把控。【方法】从互动内容文本特征分析入手,通过Top2Vec主题聚类、TextRank抽取式摘要生成和TransformerCopy生成式摘要生成,多角度呈现新媒体政务互动内容的摘要内涵。【结果】模型的ROUGE-1、ROUGE-2和ROUGE-L值分别达到22.05%、6.93%和20.96%,对比发现其效果优于Seq2Seq和Seq2Seq-Attention模型。【局限】仅获取了10部法律法规草案的微博政务互动内容,未在更广泛的新媒体政务互动内容中验证本文方法。【结论】本文方法能够揭示事件主题类别和舆情摘要,具备一定的领域适应性和应用优势。
[Objective] This paper tries to summarize interactive contents from new media for government affairs based on topic clustering, aiming to help the government effectively control public opinion events. [Methods]First, we analyzed the textual features of the interactive contents. Then, we generated abstracts for the contents with the Top2Vec, TextRank and Transformer-Copy algorithms. [Results] The proposed model’s ROUGE-1,ROUGE-2 and ROUGE-L values reached 22.05%, 6.93% and 20.96%, respectively, which were better than those of the Seq2Seq and Seq2Seq-Attention models. [Limitations] We only examined the new model with interactive contents on 10 draft laws and regulations from Sina Microblog. [Conclusions] The proposed method can summarize the topics and public opinion on specific events.
作者
胡吉明
郑翔
Hu Jiming;Zheng Xiang(School of Information Management,Wuhan University,Wuhan 430072,China;Information Retrieval and Knowledge Mining Laboratory,Wuhan University,Wuhan 430072,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2022年第6期95-104,共10页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金面上项目(项目编号:71874125)
湖北省青年拔尖人才培养计划项目的研究成果之一。
关键词
新媒体
政务互动内容
摘要生成
主题聚类
New Media
Government Affairs Interactive Content
Summarization Generation
Topic Clustering
