  We propose a method for online news stream clustering that is a variant of
the non-parametric streaming K-means algorithm. Our model uses a combination of
sparse and dense document representations, aggregates document-cluster
similarity along these multiple representations and makes the clustering
decision using a neural classifier. The weighted document-cluster similarity
model is learned using a novel adaptation of the triplet loss into a linear
classification objective. We show that the use of a suitable fine-tuning
objective and external knowledge in pre-trained transformer models yields
significant improvements in the effectiveness of contextual embeddings for
clustering. Our model achieves a new state-of-the-art on a standard stream
clustering dataset of English documents.
