Task: Summarization
Input (English): Most state-of-the-art neural machine translation systems, despite being different
 in architectural skeletons (e.g., recurrence, convolutional), share an indispensable
 feature: the Attention. However, most existing attention methods are token-based
 and ignore the importance of phrasal alignments, the key ingredient for the success
 of phrase-based statistical machine translation. In this paper, we propose
 novel phrase-based attention methods to model n-grams of tokens as attention
 entities. We incorporate our phrase-based attentions into the recently proposed
 Transformer network, and demonstrate that our approach yields improvements of
 1.3 BLEU for English-to-German and 0.5 BLEU for German-to-English translation
 tasks, and 1.75 and 1.35 BLEU points in English-to-Russian and Russian-to-English translation tasks 
 on WMT newstest2014 using WMT’16 training data.

Output (English): Paper presents an attention mechanism that computes a weighted sum over not only single tokens but ngrams(phrases).