摘要
针对当前火灾信息系统信息来源繁杂、分类解读依赖专家经验等问题,通过自动文本摘要方法来实现火灾信息文本的辅助分析。在优化消防信息数据集的基础上,采用全词遮罩的BERT_WWM中文预训练模型,提取具有上下文语义的词向量表征,并使用Transformer提取摘要句,进一步提升火灾信息摘要提取效果。通过在ROUGE-1、ROUGE-2和ROUGE-L上进行试验,研究的BERT_WWM+Transformer方法较其他基准有小幅提升,人类主观评价可部分达到提取文本关键信息的目的,为执行任务时的信息支持工作提供可用的自动化工具。
For solving the problems of the current fire information system,such as the complexity of information sources and the dependence of classification and interpretation on expert experience,this paper presented an automatic text summarization method to realize the auxiliary analysis of fire rescue text.On the basis of optimizing the fire rescue information dataset,the method contained an adopted pre-training BERT_WWM model,for extracting the word vector representation with context semantics,and used the Transformer to extract the summary sentence,so as to further improve the effect of fire information summary extraction.Through experiments in ROUGE-1,ROUGE-2 and ROUGE-L,our BERT_WWM+Transformer method was slightly improved comparing with other existed methods.Even the subjective evaluation could partially prove the purpose of extracting key information from texts,and showed that our method supported an available automation tools for intelligence analysis.
作者
李继宝
董婷婷
关斯琪
万子敬
Li Jibao;Dong Tingting;Guan Siqi;Wan Zijing(Tianjin Fire Science and Technology Research Institute of MEM,Tianjin 300381,China;Key Laboratory of Fire Protection Technology for Industry and Public Building,Ministry of Emergency Management,Tianjin 300381,China;Tianjin Key Laboratory of Fire Safety Technology,Tianjin 300381,China)
出处
《消防科学与技术》
CAS
北大核心
2023年第4期583-588,共6页
Fire Science and Technology
基金
国家重点研发计划课题(2019YFB1312105)
中央基本科研业务费项目(2022SJ22,2020SJ32,2021SJ18)。
关键词
火灾信息
BERT
摘要生成
自编码器
fire rescue intelligence
BERT
summary extraction
autoencoder
