Plausibility-promoting generative adversarial network for abstractive text summarization with multi-task constraint-Reference-Cited by-同舟云学术

Plausibility-promoting generative adversarial network for abstractive text summarization with multi-task constraint

Published:2020-06 Issue: Volume:521 Page:46-61
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Yang Min^ORCID,Wang Xintong,Lu Yao,Lv Jianming,Shen Ying^ORCID,Li Chengming^ORCID

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

SIAT Innovation Program for Excellent Young Researchers

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference43 articles.

1. S. Arora, Y. Liang, T. Ma, A simple but tough-to-beat baseline for sentence embeddings (2016).

2. Neural machine translation by jointly learning to align and translate;Bahdanau,2014

3. Improving multi-document summarization via text classification.;Cao,2017

4. Multitask Learning;Caruana,1998

5. Show, adapt and tell: adversarial training of cross-domain image captioner;Chen,2017

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