  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.
