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. 1
Suzuki et al. REF mitigated the repeatability of summaries by evaluating the upper bound frequency of each target word in the encoder and controlling the output word in the decoder.
Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization
arXiv: Computation and Language
Computer Science Mathematics