Improving German News Clustering with Contrastive Learning

Automatic news articles clustering is one of the most important tasks for news publishers. Traditional unsupervised models exploit generic text representation (e.g., BERT) and typically do not consider the relationships between each paragraph in news articles. Such depth learning from news articles is important for clustering full-length articles. Recently contrastive learning (CL) has shown to be a popular method for representation learning that uses positive and negative data pairs generated using data augmentation techniques to improve the representation in the latent space. In this work, we propose text augmentation methods and use contrastive learning to cluster daily growing full-length German news articles. Our experiments on four German news article datasets (one labeled and three unlabeled datasets) demonstrate that contrastive learning and our text augmentation methods significantly improve the representation of news articles compared to generic pre-trained text representation and have high performance for clustering tasks.