Aspect and opinion aware abstractive review summarization with reinforced hard typed decoder

• Main Authors: TIAN, Yufei, YU, Jianfei, JIANG, Jing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Natural Language Processing; Review Summarization; Text Generation; Neural Networks; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/4870; https://ink.library.smu.edu.sg/context/sis_research/article/5873/viewcontent/Aspect_Opinion_Aware_pv_oa.pdf
• Institution: Singapore Management University

In this paper, we study abstractive review summarization. Observing that review summaries often consist of aspect words, opinion words and context words, we propose a two-stage reinforcement learning approach, which first predicts the output word type from the three types, and then leverages the predicted word type to generate the final word distribution. Experimental results on two Amazon product review datasets demonstrate that our method can consistently outperform several strong baseline approaches based on ROUGE scores.