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.

翻译：实时从海量新闻流中无监督地发现具有关联新闻故事，有助于人们在无需昂贵人工标注的情况下消化海量新闻。现有无监督在线故事发现研究的主流方法是通过符号或图嵌入表示新闻文章，并逐步将其聚类为故事。近期的大语言模型有望进一步改进嵌入表示，但简单地将这些模型用于无差别编码文章中的全部信息，不足以处理文本丰富且不断演变的新闻流。本文提出了一种新颖的主题嵌入方法，利用现成的预训练句子编码器，通过考虑新闻文章和故事共享的时间主题动态表示它们。为实现无监督在线故事发现的想法，我们引入了一个可扩展框架USTORY，该框架包含两种主要技术：主题与时间感知的动态嵌入以及新颖性自适应的增量聚类，并由轻量级故事摘要驱动。基于真实新闻数据集的全面评估表明，USTORY在实现比基线方法更高的故事发现性能的同时，对各种流式数据场景均具有鲁棒性和可扩展性。