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：实时无监督发现具有相关新闻文章的故事，有助于人们在无需昂贵人工标注的情况下消化海量新闻流。现有无监督在线故事发现研究的常见方法是以符号或图嵌入表示新闻文章，并逐步将其聚类成故事。近期的大语言模型有望进一步改进嵌入，但直接采用这些模型对文章中的全部信息不加区分地编码，难以有效处理文本丰富且不断演化的新闻流。本文提出一种新颖的主题嵌入方法，利用现成的预训练句子编码器，通过考虑文章与故事共享的时间主题来动态表示二者。为实现无监督在线故事发现，我们引入可扩展框架USTORY，该框架包含两种关键技术：主题与时间感知的动态嵌入，以及基于轻量级故事摘要的新颖性感知自适应聚类。基于真实新闻数据集的全面评估表明，USTORY在故事发现性能上优于基线方法，同时具有鲁棒性和可扩展性，能适应多种流式处理场景。