A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification

TL;DR

This paper introduces a hierarchical end-to-end model that jointly improves text summarization and sentiment classification by leveraging their relatedness, resulting in better performance on Amazon reviews.

Contribution

The paper presents a novel hierarchical model that integrates sentiment classification as a further summarization step atop text summarization, enhancing both tasks.

Findings

Outperforms strong baselines on Amazon reviews datasets

Improves accuracy of sentiment classification

Enhances quality of abstractive summarization

Abstract

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.