Transformer Reasoning Network for Personalized Review Summarization

Review summarization aims to generate condensed text for online product reviews, and has attracted more and more attention in E-commerce platforms. In addition to the input review, the quality of generated summaries is highly related to the characteristics of users and products, e.g., their historical summaries, which could provide useful clues for the target summary generation. However, most previous works ignore the underlying interaction between the given input review and the corresponding historical summaries. Therefore, we aim to explore how to effectively incorporate the history information into the summary generation. In this paper, we propose a novel transformer-based reasoning framework for personalized review summarization. We design an elaborately adapted transformer network containing an encoder and a decoder, to fully infer the important and informative parts among the historical summaries in terms of the input review to generate more comprehensive summaries. In the encoder of our approach, we develop an inter- and intra-attention to involve the history information selectively to learn the personalized representation of the input review. In the decoder part, we propose to incorporate the constructed reasoning memory learning from historical summaries into the original transformer decoder, and design a memory-decoder attention module to retrieve more useful information for the final summary generation. Extensive experiments are conducted and the results show our approach could generate more reasonable summaries for recommendation, and outperform many competitive baseline methods.