An Empirical Study of Generating Texts for Search Engine Advertising (2021)

Abstract: Although there are many studies on neural language generation (NLG), few trials are put into the real world, especially in the advertising domain. Generating ads with NLG models can help copywriters in their creation. However, few studies have adequately evaluated the effect of generated ads with actual serving included because it requires a large amount of training data and a particular environment. In this paper, we demonstrate a practical use case of generating ad-text with an NLG model. Specially, we show how to improve the ads’ impact, deploy models to a product, and evaluate the generated ads.

## introduction search engine advertising (sea) displays ads relevant to the queries that users have searched on search engines as a part of the search results. on the ad creation side, ad copywriters develop keywords and ad-texts that are likely to be searched by users, and ad-texts are attractive to users based on the keywords and landing page (lp) contents. advertisers or advertising agencies then submit these keywords and ad-texts to the ad delivery service. after submission, if the user's search queries and the submitted keywords match, the service selects an ad-text from the submissions through an auction and displays it to the user. sea plays an important role in the advertising market because it can mutually satisfy users' and advertisers' respective demands. advertising agencies have many copywriters on staff; however, manual creation will eventually reach its limit with the ever-increasing content. therefore, auto-generating ads are expected to be a great support for ad creation. fill templates with words and phrases extracted from web search results or lps are commonly used in ad-text generation @xcite , @xcite , @xcite , thomaidou et al. however, unlike typical natural language generation tasks, ad-texts are determined from input and characteristics such as previous ad delivery performance, the contexts in overall ads, and the relevance between the search queries and the results. diversity is also an importance factor in this task because readers can be bored if a model generates ad-text that has already been used. therefore, to make ad-texts more attractive, the model must generate ad-texts that were not used previously. in addition, the model must consider the ad-text's length because ads are presented in a limited space, which imposes a limitation on the length in practical usage. the addition of important keywords in the ad-text is also necessary to enhance user engagement because the search result page highlights the searched keyword in the user query and the adtext. furthermore, few ad-text generation models are used in real-world applications notwithstanding the commercial domain of advertising. therefore, few studies have evaluated all end-to-end processes from generation to actual delivery. considering these requirements, we propose a method for generating ad-text by utilizing rl with rewards. this approach enables using ad-texts not included in the original training dataset through sampling from the training model. therefore, we can expect improvements in diversity for generated ad-texts. we compared models specifically constructed for ad-text generation to determine the important factors for this task. we investigated our models in a real-world application, and performed an ad-delivery evaluation. the evaluation results showed that the proposed method improved the both human-rated attractiveness and relevance scores and the ad-delivery results compared to other approaches, while also maintaining diversity of the generated ad-texts. our contributions are as follows. as shown in ## conclusion in this paper, we proposed several rewards based on rl, which can consider the various characteristics of ad-texts. in experiments, ad-texts generated from seq2seq incorporated with these rewards achieved better automatic, human, and ad-delivery evaluation results than the basic seq2seq methods. our analysis showed that considering results from the keyword-related automatic evaluation metric and the fluency by crowdsourcing workers is important for generating effective ad-texts. as further work, we plan to consider diversity as a reward to generate more diverse ad-texts.