Given the importance of identifying and monitoring news stories within the continuous flow of news articles, this paper presents PromptStream, a novel method for unsupervised news story discovery. In order to identify coherent and comprehensive stories across the stream, it is crucial to create article representations that incorporate as much topic-related information from the articles as possible. PromptStream constructs these article embeddings using cloze-style prompting. These representations continually adjust to the evolving context of the news stream through self-supervised learning, employing a contrastive loss and a memory of the most confident article-story assignments from the most recent days. Extensive experiments with real news datasets highlight the notable performance of our model, establishing a new state of the art. Additionally, we delve into selected news stories to reveal how the model’s structuring of the article stream aligns with story progression.

A new trend in topic modeling research is to do Neural Topic Modeling by Clustering document Embeddings (NTM-CE) created with a pretrained language model. Studies have evaluated static NTM-CE models and found them performing comparably to, or even better than other topic models. An important extension of static topic modeling is making the models dynamic, allowing the study of topic evolution over time, as well as detecting emerging and disappearing topics. In this research proposal, we present two research questions to understand dynamic topic modeling with NTM-CE theoretically and practically. To answer these, we propose four phases with the aim of establishing evaluation methods for dynamic topic modeling, finding NTM-CE-specific properties, and creating a framework for dynamic NTM-CE. For evaluation, we propose to use both quantitative measurements of coherence and human evaluation supported by our recently developed tool.

Topic models are powerful tools to get an overview of large collections of text data, a situation that is prevalent in industry applications. A rising trend within topic modeling is to directly cluster dimension-reduced embeddings created with pretrained language models. It is difficult to evaluate these models because there is no ground truth and automatic measurements may not mimic human judgment. To address this problem, we created a tool called STELLAR for interactive topic browsing which we used for human evaluation of topics created from a real-world dataset used in industry. Embeddings created with BERT were used together with UMAP and HDBSCAN to model the topics. The human evaluation found that our topic model creates coherent topics. The following discussion revolves around the requirements of industry and what research is needed for production-ready systems.