Towards Personalized Review Summarization via User-Aware Sequence Network

We address personalized review summarization, which generates a condensed summary for a user’s review, accounting for his preference on different aspects or his writing style. We propose a novel personalized review summarization model named User-aware Sequence Network (USN) to consider the aforementioned users’ characteristics when generating summaries, which contains a user-aware encoder and a useraware decoder. Specifically, the user-aware encoder adopts a user-based selective mechanism to select the important information of a review, and the user-aware decoder incorporates user characteristic and user-specific word-using habits into word prediction process to generate personalized summaries. To validate our model, we collected a new dataset Trip, comprising 536, 255 reviews from 19, 400 users. With quantitative and human evaluation, we show that USN achieves state-ofthe-art performance on personalized review summarization.

Authors

• Junjie Li
• Haoran Li Institute of Automation, Chinese Academy of Sciences & School of Artificial Intelligence, University of Chinese Academy of Sciences
• Chengqing Zong State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

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