Search Results for author: Junnan Liu

Found 3 papers, 1 papers with code

Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization

1 code implementation COLING 2022 Yiming Wang, Qianren Mao, Junnan Liu, Weifeng Jiang, Hongdong Zhu, JianXin Li

Labeling large amounts of extractive summarization data is often prohibitive expensive due to time, financial, and expertise constraints, which poses great challenges to incorporating summarization system in practical applications.

Extractive Summarization

Noised Consistency Training for Text Summarization

no code implementations28 May 2021 Junnan Liu, Qianren Mao, Bang Liu, Hao Peng, Hongdong Zhu, JianXin Li

In this paper, we argue that this limitation can be overcome by a semi-supervised approach: consistency training which is to leverage large amounts of unlabeled data to improve the performance of supervised learning over a small corpus.

Abstractive Text Summarization

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