Both select high quality parses by computing the level of agreement among different parser outputs: wheras the former uses several versions of a constituency parser, each trained on a different sample from the training data, the latter uses the parses produced by different dependency parsing algorithms trained on the same data.
a widely acknowledged problem of both supervised-based and ensemble-based methods is that they are dramatically influenced by a) the selection of the training data and b) the accuracy and the typology of errors of the used parser.