Multiple Knowledge-Enhanced Meteorological Social Briefing Generation

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 $\text {ROUGE-1}$ , $\text {ROUGE-2}$ , and $\text {ROUGE-L}$ scores, and in sentiment evaluation, as measured by $F_{1}$ 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).