SUMT: A Framework of Summarization and MT

We present a novel system combination of machine translation and text summariza-tion which provides high quality summary translations superior to the baseline trans-lation of the entire document. We first use supervised learning and build a classifier that predicts if the translation of a sentence has high or low translation quality. This is a reference-free estimation of MT qual-ity which helps us to distinguish the subset of sentences which have better translation quality. We pair this classifier with a state-of-the-art summarization system to build an MT-aware summarization system. To evaluate summarization quality, we build a test set by summarizing a bilingual corpus. We evaluate the performance of our sys-tem with respect to both MT and summa-rization quality and, demonstrate that we can balance between improving MT qual-ity and maintaining a decent summariza-tion quality.