Multiple knowledge-enhanced meteorological social briefing generation

Article

Shi, Kaize, Peng, Xueping, Lu, Hao, Zhu, Yifan and Niu, Zhendong. 2023. "Multiple knowledge-enhanced meteorological social briefing generation." IEEE Transactions on Computational Social Systems. 11 (2), pp. 2002-2013. https://doi.org/10.1109/TCSS.2023.3298252
Article Title

Multiple knowledge-enhanced meteorological social briefing generation

ERA Journal ID212762
Article CategoryArticle
AuthorsShi, Kaize, Peng, Xueping, Lu, Hao, Zhu, Yifan and Niu, Zhendong
Journal TitleIEEE Transactions on Computational Social Systems
Journal Citation11 (2), pp. 2002-2013
Number of Pages12
Year2023
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN2329-924X
Digital Object Identifier (DOI)https://doi.org/10.1109/TCSS.2023.3298252
Web Address (URL)https://ieeexplore.ieee.org/abstract/document/10206439
Abstract

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).

KeywordsControllable text generation; ecision support service; emergency management; meteorological social briefing; natural disaster; social weather
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
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Byline AffiliationsUniversity of Technology Sydney
Chinese Academy of Sciences, China
Tsinghua University, China
University of Pittsburgh, United States
Beijing Institute of Technology, China
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