Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization
Jun Suzuki, Masaaki Nagata · 2017
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.