Extractive speech summarization can be thought of as a decision-making process where the summarizer attempts to select a subset of informative sentences from the original document. Meanwhile, a sentence being selected as part of a summary is typically determined by three primary factors: significance, relevance and redundancy. To meet these specifications, we recently presented a novel probabilistic framework stemming from the Bayes decision theory for extractive speech summarization. It not only inherits the merits of several existing summarization techniques but also provides a flexible mechanism to render the redundancy and coherence relationships among sentences and between sentences and the whole document, respectively. In this paper, we propose several new approaches to the ranking strategy and modeling paradigm involved in such a framework. All experiments reported were carried out on a broadcast news speech summarization task; very promising results were demonstrated.