TitleA hierarchical end-to-end model for jointly improving text summarization and sentiment classification
AuthorsMa, Shuming
Sun, Xu
Lin, Junyang
Ren, Xuancheng
AffiliationMOE Key Lab of Computational Linguistics, School of EECS, Peking University, China
School of Foreign Languages, Peking University, China
Issue Date2018
Publisher27th International Joint Conference on Artificial Intelligence, IJCAI 2018
Citation27th International Joint Conference on Artificial Intelligence, IJCAI 2018. 2018, 2018-July, 4251-4257.
AbstractText summarization and sentiment classification both aim to capture the main ideas of the text but at different levels. Text summarization is to describe the text within a few sentences, while sentiment classification can be regarded as a special type of summarization which 'summarizes' the text into a even more abstract fashion, i.e., a sentiment class. Based on this idea, we propose a hierarchical endto-end model for joint learning of text summarization and sentiment classification, where the sentiment classification label is treated as the further 'summarization' of the text summarization output. Hence, the sentiment classification layer is put upon the text summarization layer, and a hierarchical structure is derived. Experimental results on Amazon online reviews datasets show that our model achieves better performance than the strong baseline systems on both abstractive summarization and sentiment classification.1. © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.
Appears in Collections:信息科学技术学院

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