The news landscape is continuously evolving, with an ever-increasing volume
of information from around the world. Automated event detection within this
vast data repository is essential for monitoring, identifying, and categorizing
significant news occurrences across diverse platforms. This paper presents an
event detection framework that leverages Large Language Models (LLMs) combined
with clustering analysis to detect news events from the Global Database of
Events, Language, and Tone (GDELT). The framework enhances event clustering
through both pre-event detection tasks (keyword extraction and text embedding)
and post-event detection tasks (event summarization and topic labeling). We
also evaluate the impact of various textual embeddings on the quality of
clustering outcomes, ensuring robust news categorization. Additionally, we
introduce a novel Cluster Stability Assessment Index (CSAI) to assess the
validity and robustness of clustering results. CSAI utilizes latent feature
vectors to provide a new way of measuring clustering quality. Our experiments
indicate that combining LLM embeddings with clustering algorithms yields the
best results, demonstrating greater robustness in terms of CSAI scores.
Moreover, post-event detection tasks generate meaningful insights, facilitating
effective interpretation of event clustering results. Overall, our experimental
results indicate that the proposed framework offers valuable insights and could
enhance the accuracy and depth of news reporting.

通过结合大型语言模型和聚类分析，利用自动化事件检测框架从全球事件、语言和情感数据库（GDELT）中检测新闻事件，并通过关键词提取、文本嵌入、事件摘要和主题标签等任务加强事件聚类，以提供有价值的洞察力和增强新闻报道的准确性和深度。