ABSTRACT :  Automated Knowledge Discovery is an active area of research that seeks to address the need for extensive knowledge acquisition and elicitation, curation and archival for large quantities of text. A generic, flexible and extendable text analytics framework is based on robust theme detection methods. A novel method is described here to extract thematic hierarchies using the Latent Dirichlet Allocation (LDA) topic models, noun-phrase extraction and phrase filtering heuristics. Further, a visual representation of theme dynamics, the "Document Thematic Map (DTmap)", is created to enable text segmentation using the theme-mix.