Stumps, classification trees with only one split at the root node, have been shown by Schapire and Singer to be an effective method for text categorization when embedded in a boosting algorithm as its base classifiers. In their experiments, the splitting point (the partition) of each stump is decided by whether a certain term appears or not in a text document, which is too weak to obtain satisfied accuracy even after they are combined by boosting, and therefore the iteration times needed by boosting is sharply increased as an indicator of low efficiency. To improve these base classifiers, an idea is proposed in this paper to decide the splitting point of each stump by all the terms of a text document. Specifically, it employs the numerical relationship between the similarities of the VSM-vector of text document and the representational VSM-vector of each class as the partition criteria of the base classifiers. Meanwhile, to further facilitate its convergence, the boosting weights assigned to sample documents are introduced to the computation of representational VSM-vectors for possible classes dynamically. Experimental results show that the algorithm is both more efficient for training and more effective than its predecessor for fulfilling text categorization tasks. This trend seems more conspicuous along with the incensement of problem scale.