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