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2017 EACL EACL 2017

Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization

Abstract

AbstractThis 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.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — redundant generation
🐣 Hot Topic Early Bird — abstractive summarization
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Jun Suzuki , Masaaki Nagata

Topics

Deep Learning > Architectures > Transformers Deep Learning > Architectures > Neural Networks Natural Language Processing > Generation > Summarization Machine Learning > Learning Types > Representation Learning Deep Learning > Learning Types > Deep Learning

Keywords

neural text generation redundancy reduction recurrent neural network abstractive summarization encoder-decoder model redundant generation rnn encoder-decoder vocabulary estimation vocabulary control
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