A Multi-Task Learning Framework for Abstractive Text Summarization

We propose a Multi-task learning approach for Abstractive Text Summarization (MATS), motivated by the fact that humans have no difficulty performing such task because they have the capabilities of multiple domains. Specifically, MATS consists of three components: (i) a text categorization model that learns rich category-specific text representations using a bi-LSTM encoder; (ii) a syntax labeling model that learns to improve the syntax-aware LSTM decoder; and (iii) an abstractive text summarization model that shares its encoder and decoder with the text categorization and the syntax labeling tasks, respectively. In particular, the abstractive text summarization model enjoys significant benefit from the additional text categorization and syntax knowledge. Our experimental results show that MATS outperforms the competitors. 1

Authors

• Yao Lu Harbin Institute of Technology, Shenzhen
• Linqing Liu
• Zhile Jiang
• Min Yang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China
• Randy Goebel Department of Computing Science, University of Alberta, Edmonton, Canada Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Canada

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