Unsupervised discovery of stories with correlated news articles in real-time
helps people digest massive news streams without expensive human annotations. A
common approach of the existing studies for unsupervised online story discovery
is to represent news articles with symbolic- or graph-based embedding and
incrementally cluster them into stories. Recent large language models are
expected to improve the embedding further, but a straightforward adoption of
the models by indiscriminately encoding all information in articles is
ineffective to deal with text-rich and evolving news streams. In this work, we
propose a novel thematic embedding with an off-the-shelf pretrained sentence
encoder to dynamically represent articles and stories by considering their
shared temporal themes. To realize the idea for unsupervised online story
discovery, a scalable framework USTORY is introduced with two main techniques,
theme- and time-aware dynamic embedding and novelty-aware adaptive clustering,
fueled by lightweight story summaries. A thorough evaluation with real news
data sets demonstrates that USTORY achieves higher story discovery performances
than baselines while being robust and scalable to various streaming settings.

该研究提出了一种基于主题嵌入、动态嵌入和自适应聚类的框架 USTORY 实现了无监督在线故事发现，并在真实新闻数据集上进行了全面评估，结果表明 USTORY 的性能比基线更高，并且具有鲁棒性和可扩展性。