An information-geometric approach for document similarities in the framework of “Probabilistic Latent Semantic Indexing” was first proposed by T. Hofmann (2000) and later extended (“revisited”) by Nyffenegger et al. (2006). This paper presents an in-depth study and revision of these models by (1) providing a simpler unified description framework, (2) investigating the role of the Fisher Information Matrix G(θ), and (3) analyzing the impact of latent “topic” parameters in such models. It furthermore provides new experimental results on larger collections coming from the TREC–AP evaluation corpus.