Towards Personalized Review Summarization via User-Aware Sequence Network


  • Junjie Li Chinese Academy of Sciences
  • Haoran Li Chinese Academy of Sciences
  • Chengqing Zong Chinese Academy of Sciences



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.




How to Cite

Li, J., Li, H., & Zong, C. (2019). Towards Personalized Review Summarization via User-Aware Sequence Network. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 6690-6697.



AAAI Technical Track: Natural Language Processing