TY - JOUR AU - Lu, Yao AU - Liu, Linqing AU - Jiang, Zhile AU - Yang, Min AU - Goebel, Randy PY - 2019/07/17 Y2 - 2024/03/28 TI - A Multi-Task Learning Framework for Abstractive Text Summarization JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - Student Abstract Track DO - 10.1609/aaai.v33i01.33019987 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5130 SP - 9987-9988 AB - <p>We propose a Multi-task learning approach for Abstractive Text Summarization (<em>MATS</em>), motivated by the fact that humans have no difficulty performing such task because they have the capabilities of multiple domains. Specifically, <em>MATS</em> 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 <em>MATS</em> outperforms the competitors.<sup>1</sup></p> ER -