Cutting-off redundant repeating generations for neural abstractive summarization

Jun Suzuki, Masaaki Nagata

研究成果: Conference contribution

17 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルShort Papers
出版社Association for Computational Linguistics (ACL)
ページ291-297
ページ数7
ISBN(電子版)9781510838604
DOI
出版ステータスPublished - 2017
外部発表はい
イベント15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain
継続期間: 2017 4 32017 4 7

出版物シリーズ

名前15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference
2

Other

Other15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
国/地域Spain
CityValencia
Period17/4/317/4/7

ASJC Scopus subject areas

  • 言語学および言語
  • 言語および言語学

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