SuMT: A Framework of Summarization and MT

We present a novel system combination of machine translation and text summarization which provides high quality summary translations superior to the baseline translation 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 quality which helps us to distinguish the subset of sentences which have better translation quality. We pair this classifier with a stateof-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 system with respect to both MT and summarization quality and, demonstrate that we can balance between improving MT quality and maintaining a decent summarization quality.