A Manifold-Aware Topic Modeling Approach via Rank-Based Prototypes

Recent topic models leverage pretrained embeddings, but neural architectures produce latent representations without grounding in specific texts, and clustering-based pipelines assign representative documents only post hoc, relying on absolute distances distorted by hubness and anisotropy in high-dimensional spaces.We introduce MARETopic, a training-free framework that casts topic discovery as rankbased prototype selection.After projecting embeddings onto a low-dimensional manifold, MARETopic builds ranked lists encoding ordinal neighborhood structure.A greedy algorithm selects exactly K exemplar documents, real corpus texts, whose neighborhoods cover the corpus.Two variants share this criterion.MARETopic Corr scores candidates with a query performance predictor and a rank correlation measure, leading Purity and NMI on the two benchmarks with the most categories, ahead of both neural and clustering-based topic models.MARETopic Diff scores them with a rank-based diffusion matrix, needs neither measure, and runs 1.7 to 1.9 times faster.Without a single gradient update, MARETopic leads topic coherence on two of three datasets.A novel intertopic Maximal Marginal Relevance step raises vocabulary diversity at little cost in coherence.