These features can be integrated in a systematic way into the proposed framework by taking the advantage of the learning capability of the supervised machine-learning methods.
the prior probability   j S P can be approximated by: where   are the likelihoods that a sentence j S with features j X are generated by the summary class S and the nonsummary class S , respectively; the prior probability   , several popular supervised classifiers (or summarizers), like BC or SVM, can be leveraged for this purpose.