  This paper tackles the reduction of redundant repeating generation that is
often observed in RNN-based encoder-decoder models. Our basic idea is to
jointly estimate the upper-bound frequency of each target vocabulary in the
encoder and control the output words based on the estimation in the decoder.
Our method shows significant improvement over a strong RNN-based
encoder-decoder baseline and achieved its best results on an abstractive
summarization benchmark.
