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Recently, neural topic models (NTMs) have been incorporated into pre-trained language models (PLMs), to capture the global semantic information for text summarization.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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A probabilistic approach to semantic representation
Thomas L Griffiths and Mark Steyvers. 2002 · 2002
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Automatic evaluation of summaries using n-gram co-occurrence statistics
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Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020 · 2004
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Min Peng, Qianqian Xie, Yanchun Zhang, Hua Wang, Xiuzhen Jenny Zhang, Jimin Huang, and Gang Tian. 2018 · 2018
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Ming Zhong, Pengfei Liu, Yiran Chen, Danqing Wang, Xipeng Qiu, and Xuan-Jing Huang. 2020 · 2020
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Xiyan Fu, Jun Wang, Jinghan Zhang, Jinmao Wei, and Zhenglu Yang. 2020 · 2020
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Friendly topic assistant for transformer based abstractive summarization
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Luyang Huang, Shuyang Cao, Nikolaus Parulian, Heng Ji, and Lu Wang. 2021 · 2021
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Ye Liu, Jian-Guo Zhang, Yao Wan, Congying Xia, Lifang He, and Philip S Yu. 2021 · 2021
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