Keyword Conditional Variational Autoencoder for advertising headline generation

Abstract

Recently, fast and massive advertising headline production is highly demanded by growing number of digital ad. Many types of headline generation systems have been developed. But, most of these systems generate headlines systematically by rules and lack generation variety. On the hands, the systems which generate headlines almost randomly satisfy such variety but these headlines are not relevant to ad objective. Until now, it is still difficult to satisfy both variety and relevancy. To this end, we propose Keyword Conditional Variational Autoencoder for advertising headline generation. We regulate generation process by relevant keyword while keeping variety by randomly selected input hidden variables. It can generate variety of headlines and obtain headlines which include a relevant keyword.