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Online advertising is an important revenue source for many IT companies. Inthe search advertising scenario, advertisement text that meets the need of thesearch query would be more attractive to the user. However, the manual creationof query-variant advertisement texts for massive items is expensive.Traditional text generation methods tend to focus on the general searchingneeds with high frequency while ignoring the diverse personalized searchingneeds with low frequency. In this paper, we propose the query-variantadvertisement text generation task that aims to generate candidateadvertisement texts for different web search queries with various needs basedon queries and item keywords. To solve the problem of ignoring low-frequencyneeds, we propose a dynamic association mechanism to expand the receptive fieldbased on external knowledge, which can obtain associated words to be added tothe input. These associated words can serve as bridges to transfer the abilityof the model from the familiar high-frequency words to the unfamiliarlow-frequency words. With association, the model can make use of variouspersonalized needs in queries and generate query-variant advertisement texts.Both automatic and human evaluations show that our model can generate moreattractive advertisement text than baselines.

Query-Variant Advertisement Text Generation with Association Knowledge