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Unsupervised discovery of stories with correlated news articles in real-timehelps people digest massive news streams without expensive human annotations. Acommon approach of the existing studies for unsupervised online story discoveryis to represent news articles with symbolic- or graph-based embedding andincrementally cluster them into stories. Recent large language models areexpected to improve the embedding further, but a straightforward adoption ofthe models by indiscriminately encoding all information in articles isineffective to deal with text-rich and evolving news streams. In this work, wepropose a novel thematic embedding with an off-the-shelf pretrained sentenceencoder to dynamically represent articles and stories by considering theirshared temporal themes. To realize the idea for unsupervised online storydiscovery, 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 newsdata sets demonstrates that USTORY achieves higher story discovery performancesthan baselines while being robust and scalable to various streaming settings.

Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embedding