  In product description generation (PDG), the user-cared aspect is critical
for the recommendation system, which can not only improve user's experiences
but also obtain more clicks. High-quality customer reviews can be considered as
an ideal source to mine user-cared aspects. However, in reality, a large number
of new products (known as long-tailed commodities) cannot gather sufficient
amount of customer reviews, which brings a big challenge in the product
description generation task. Existing works tend to generate the product
description solely based on item information, i.e., product attributes or title
words, which leads to tedious contents and cannot attract customers
effectively. To tackle this problem, we propose an adaptive posterior network
based on Transformer architecture that can utilize user-cared information from
customer reviews. Specifically, we first extend the self-attentive Transformer
encoder to encode product titles and attributes. Then, we apply an adaptive
posterior distillation module to utilize useful review information, which
integrates user-cared aspects to the generation process. Finally, we apply a
Transformer-based decoding phase with copy mechanism to automatically generate
the product description. Besides, we also collect a large-scare Chinese product
description dataset to support our work and further research in this field.
Experimental results show that our model is superior to traditional generative
models in both automatic indicators and human evaluation.
