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Parallel cross-lingual summarization data is scarce, requiring models to better use the limited available cross-lingual resources.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J Liu. 2019a · 1912
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Cross-language document summarization based on machine translation quality prediction
Xiaojun Wan, Huiying Li, and Jianguo Xiao. 2010 · 2010
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Using bilingual information for cross-language document summarization
Xiaojun Wan. 2011 · 2011
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Multi-task transfer methods to improve one-shot learning for multimedia event detection
Wang Yan, Jordan Yap, and Greg Mori. 2015 · 2015
Earlier work this paper cites.
Phrase-based compressive cross-language summarization
Jin-ge Yao, Xiaojun Wan, and Jianguo Xiao. 2015 · 2015
Earlier work this paper cites.
Abstractive cross-language summarization via translation model enhanced predicate argument structure fusing
Jiajun Zhang, Yu Zhou, and Chengqing Zong. 2016 · 2016
Earlier work this paper cites.
Best-worst scaling more reliable than rating scales: A case study on sentiment intensity annotation
Svetlana Kiritchenko and Saif Mohammad. 2017 · 2017
Earlier work this paper cites.
Few-shot adversarial domain adaptation
Saeid Motiian, Quinn Jones, Seyed Mehdi Iranmanesh, and Gianfranco Doretto. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Meta-learning for low-resource neural machine translation
Jiatao Gu, Yong Wang, Yun Chen, Victor OK Li, and Kyunghyun Cho. 2018 · 2018
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Zero-shot cross-lingual neural headline generation
Shi-qi Shen, Yun Chen, Cheng Yang, Zhi-yuan Liu, Mao-song Sun, et al. 2018 · 2018
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Msmo: Multimodal summarization with multimodal output
Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang, and Chengqing Zong. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Mass: Masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019 · 2019
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Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov. 2019 · 2019
Later among the works it cites.
Ncls: Neural cross-lingual summarization
Junnan Zhu, Qian Wang, Yining Wang, Yu Zhou, Jiajun Zhang, Shaonan Wang, and Chengqing Zong. 2019 · 2019
Later among the works it cites.
Jointly learning to align and summarize for neural cross-lingual summarization
Yue Cao, Hui Liu, and Xiaojun Wan. 2020 · 2020
Later among the works it cites.
Cross-lingual natural language generation via pre-training
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Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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Are sixteen heads really better than one?
Paul Michel, Omer Levy, and Graham Neubig. 2019 · 2019
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Zero-shot cross-lingual abstractive sentence summarization through teaching generation and attention
Xiangyu Duan, Mingming Yin, Min Zhang, Boxing Chen, and Weihua Luo. 2019a
Cited in the paper.
Zero-shot cross-lingual abstractive sentence summarization through teaching generation and attention
Xiangyu Duan, Mingming Yin, Min Zhang, Boxing Chen, and Weihua Luo. 2019b
Cited in the paper.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019b
Cited in the paper.
Zewen Chi, Li Dong, Furu Wei, Wenhui Wang, Xian-Ling Mao, and Heyan Huang. 2020 · 2020
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Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni. 2020 · 2020
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Attend, translate and summarize: An efficient method for neural cross-lingual summarization
Junnan Zhu, Yu Zhou, Jiajun Zhang, and Chengqing Zong. 2020 · 2020
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A robust abstractive system for cross-lingual summarization
Jessica Ouyang, Boya Song, and Kathy McKeown. 2019 · 2031
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