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We propose a semi-supervised learning method called Cformer for automatic clustering of text documents in cases where clusters are described by a small number of labeled examples, while the majority of training examples are unlabeled. We motivate this setting with an application in contextual programmatic advertising, a type of content placement on news pages that does not exploit personal information about visitors but relies on the availability of a high-quality clustering computed on the basis of a small number of labeled samples.

To enable text clustering with little training data, Cformer leverages the teacher-student architecture of Meta Pseudo Labels. In addition to unlabeled data, Cformer uses a small amount of labeled data to describe the clusters aimed at. Our experimental results confirm that the performance of the proposed model improves the state-of-the-art if a reasonable amount of labeled data is available. The models are comparatively small and suitable for deployment in constrained environments with limited computing resources. The source code is available at https://github.com/Aha6988/Cformer.

We introduce ADCluster, a deep document clustering approach based on language models that is trained to adapt to the clustering task. This adaptability is achieved through an iterative process where K-Means clustering is applied to the dataset, followed by iteratively training a deep classifier with generated pseudo-labels – an approach referred to as inner adaptation. The model is also able to adapt to changes in the data as new documents are added to the document collection. The latter type of adaptation, outer adaptation, is obtained by resuming the inner adaptation when a new chunk of documents has arrived. We explore two outer adaptation strategies, namely accumulative adaptation (training is resumed on the accumulated set of all documents) and non-accumulative adaptation (training is resumed using only the new chunk of data). We show that ADCluster outperforms established document clustering techniques on medium and long-text documents by a large margin. Additionally, our approach outperforms well-established baseline methods under both the accumulative and non-accumulative outer adaptation scenarios.

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.