E-commerce sites include advertising slogans along with information regarding items. Slogans can attract viewers' attention to increase sales or visits by emphasizing advantages of items. The aim of this study is to generate a slogan from a description of an item. To generate a slogan, we apply an encoder-decoder model which has shown effectiveness in many kinds of natural language generation tasks, such as abstractive summarization. However, slogan generation task has three characteristics that distinguish it from other natural language generation tasks: distinctiveness, topic emphasis, and style difference. To handle these three characteristics, we propose a compressed representation-based reconstruction model with refer-attention and conversion layers. The results of experiments with automatic and human evaluations indicate that our method achieves higher performance than conventional methods.
展开▼
