Data Mining and Knowledge Discovery vol:26 issue:3 pages:533-558
We study several techniques for representing, fusing and comparing content representations of news documents. As underlying models we consider the vector space model (both in a term setting and in a latent semantic analysis setting) and probabilistic topic models based on latent Dirichlet allocation. Content terms can be classified as topical terms or named entities, yielding several models for content fusion and comparison. All used methods are completely unsupervised. We find that simple methods can still outperform the current state-of-the-art techniques.