Multiple Knowledge-Enhanced Meteorological Social Briefing Generation

doi:10.1109/TCSS.2023.3298252

	Multiple Knowledge-Enhanced Meteorological Social Briefing Generation
	Shi, Kaize 1,2; Peng, Xueping ; Lu, Hao3,4 ; Zhu, Yifan 5; Niu, Zhendong 1,6
发表期刊	IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
ISSN	2329-924X
	2023-08-03
页码	12
通讯作者	Niu, Zhendong(zniu@bit.edu.cn)
摘要	Frequent meteorological disasters present new challenges for decision-making in disaster response. As a timely and effective source of intelligent information, social media plays a vital role in detecting and monitoring these situations. Meteorological social briefings summarize valuable information from numerous social media posts, providing essential decision-support services. This article proposes a multi-knowledge-enhanced summarization (MKES) model for automatically generating meteorological social briefing content from multiple Sina Weibo posts. The MKES model consists of a summary generation module and a knowledge enhancement module. The knowledge enhancement module guides and constrains the summary generation process using meteorological events and geographical location knowledge, resulting in summaries that focus on describing specific knowledge from the source text. The MKES model outperforms baseline models in content evaluation, as measured by ROUGE-1, ROUGE-2, and ROUGE-L scores, and in sentiment evaluation, as measured by F1 scores. Based on the MKES model, a framework for generating meteorological social briefings is developed, providing decision support services for the China Meteorological Administration (CMA).
关键词	Controllable text generation decision support service emergency management meteorological social briefing natural disaster social weather
DOI	10.1109/TCSS.2023.3298252
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62272048] ; National Key Research and Development Program of China[2019YFB1406302]
项目资助者	National Natural Science Foundation of China ; National Key Research and Development Program of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Cybernetics ; Computer Science, Information Systems
WOS记录号	WOS:001043270800001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54006
专题	多模态人工智能系统全国重点实验室
通讯作者	Niu, Zhendong
作者单位	1.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China 2.Univ Technol Sydney, Australian Artificial Intelligence Inst, Sydney, NSW 2007, Australia 3.Chinese Acad Sci, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 4.Chinese Acad Sci, State Key Lab Management & Controlof Complex Syst, Inst Automat, Beijing 100190, Peoples R China 5.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China 6.Univ Pittsburgh, Sch Comp & Informat, Pittsburgh, PA 15260 USA
推荐引用方式 GB/T 7714	Shi, Kaize,Peng, Xueping,Lu, Hao,et al. Multiple Knowledge-Enhanced Meteorological Social Briefing Generation[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2023:12.
APA	Shi, Kaize,Peng, Xueping,Lu, Hao,Zhu, Yifan,&Niu, Zhendong.(2023).Multiple Knowledge-Enhanced Meteorological Social Briefing Generation.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,12.
MLA	Shi, Kaize,et al."Multiple Knowledge-Enhanced Meteorological Social Briefing Generation".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2023):12.