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.- Anthology ID:
- E17-2047
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 291–297
- Language:
- URL:
- https://aclanthology.org/E17-2047
- DOI:
- Cite (ACL):
- Jun Suzuki and Masaaki Nagata. 2017. Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 291–297, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization (Suzuki & Nagata, EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/E17-2047.pdf
- Data
- DUC 2004