Title Generation for Machine-Translated Documents

In this paper, we present and compare automatically generated titles for machine-translated documents using several different statistics-based methods. A Naïve Bayesian, a K-Nearest Neighbour, a TF-IDF and an it-erative Expectation-Maximization method for title gen-eration were applied to 1000 original English news documents and again to the same documents translated from English into Portuguese, French or German and back to English using SYSTRAN. The AutoSummari-zation function of Microsoft Word was used as a base line. Results on several metrics show that the statistics-based methods of title generation for machine-translated documents are fairly language independent and title generation is possible at a level approaching the accuracy of titles generated for the original English documents