@inproceedings{2aa5ef82f41b44099d6f69710411179d,
title = "Cutting-off redundant repeating generations for neural abstractive summarization",
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.",
author = "Jun Suzuki and Masaaki Nagata",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computational Linguistics.; 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 ; Conference date: 03-04-2017 Through 07-04-2017",
year = "2017",
doi = "10.18653/v1/e17-2047",
language = "English",
series = "15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "291--297",
booktitle = "Short Papers",
address = "United States",
}