[Objective] This paper aims to quickly generate real-time promotional book summaries and reduce the consumption of workforce and resources. [Methods] First, we constructed a dataset with the crawled book information based on prompt learning. Then, we used data enhancement and keyword extraction to increase information and generated the primary promotion language with the T5 PEGASUS. When the number of book reviews reaches the threshold, the summary of the book reviews will also be added. [Results] Compared with the optimal baseline model, the Rouge-1、Rouge-2、and Rouge-L scores of the proposed model were improved by 29.0%, 37.6%, and 31.9%, respectively. Adding the summary of book reviews can reflect the interests of users. [Conclusions] The proposed model could generate summaries based on the characteristics of the book corpus and has practical value.
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