  Text 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 end-to-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.
