TY - GEN
T1 - Cutting-off redundant repeating generations for neural abstractive summarization
AU - Suzuki, Jun
AU - Nagata, Masaaki
N1 - Publisher Copyright:
© 2017 Association for Computational Linguistics.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85021638333&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021638333&partnerID=8YFLogxK
U2 - 10.18653/v1/e17-2047
DO - 10.18653/v1/e17-2047
M3 - Conference contribution
AN - SCOPUS:85021638333
T3 - 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference
SP - 291
EP - 297
BT - Short Papers
PB - Association for Computational Linguistics (ACL)
T2 - 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
Y2 - 3 April 2017 through 7 April 2017
ER -