Using maximum entropy for sentence extraction

A maximum entropy classifier can be used to extract sentences from documents. Experiments using technical documents show that such a classifier tends to treat features in a categorical manner. This results in performance that is worse than when extracting sentences using a naive Bayes classifier. Addition of an optimised prior to the maximum entropy classifier improves performance over and above that of naive Bayes (even when naive Bayes is also extended with a similar prior). Further experiments show that, should we have at our disposal extremely informative features, then maximum entropy is able to yield excellent results. Naive Bayes, in contrast, cannot exploit these features and so fundamentally limits sentence extraction performance.