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arXiv · 1701.00138

Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization

Jun Suzuki·Masaaki Nagata
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Abstract

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.

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Jun Suzuki, Masaaki Nagata. 2016-12-31. Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization. https://arxiv.org/abs/1701.00138

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