Generating Attractive Ad Text by Facilitating the Reuse of Landing Page Expressions (2024)

Abstract: Ad text generation is vital for automatic advertising in various fields through search engine advertising (SEA) to avoid the cost problem caused by laborious human efforts for creating ad texts. Even though ad creators create the landing page (LP) for advertising and we can expect its quality, conventional approaches with reinforcement learning (RL) mostly focus on advertising keywords rather than LP information. This work investigates and shows the effective usage of LP information as a reward in RL-based ad text generation through automatic and human evaluations. Our analysis of the actually generated ad text shows that LP information can be a crucial reward by appropriately scaling its value range to improve ad text generation performance.

## introduction with the growth of e-commerce, online advertising, which provides useful and appealing information about products or services to users becomes an important field. search engine advertising (sea) has played an important role as an online advertising approach. in sea, an advertiser first specifies a landing page (lp), a web page to be advertised, advertising keywords, and their ad text consisting of a title and description. then, by taking into account the similarity between a search query entered by a user and the advertising keywords, a link to an lp considered appropriate for users' interests is presented to the users. at that time, sea presents the ad text with the link so that the user can decide whether to click the link. although sea has various advantages in automatically distributing advertisements that match users' interests, it has a cost problem for advertisers. in preparing ad texts, ad text writers need to create them for each advertising keyword for different lps. to create ad texts that match users' interests for advertising the target lp, they must investigate what kinds of ad texts attract users for each target product and service. thus, it is not practical to manually create ad texts for a wide range of fields. one solution to this issue is ad text generation. it automatically generates appropriate ad texts for an lp. in recent years, a lot of research @xcite has been conducted on ad text generation for sea. after templatebased approaches @xcite @xcite , sequenceto-sequence (seq2seq)-based generation methods @xcite have been widely used in ad text generation @xcite @xcite as in other nlp fields. however, maximum likelihood estimation (mle), commonly used for training seq2seq models by mimicking training data, is unsuitable for ad text generation, requiring originality and diversity we aim to create a model that generates ad texts that are attractive and relevant to the input for readers by appropriately reusing expressions within the landing page, as demonstrated in this example. some previous studies have relied on reinforcement learning (rl) to deal with this problem. in rl, models learn to follow rewards built explicitly for a target task rather than to mimic the training data. thus, we can reflect specific characteristics for ad text into generated texts through the rewards. for the reward in ad text generation with seq2seq models, @xcite focus on click-through rates for ad texts and @xcite focus on feedback from sea to enhance the quality of generated ad texts. although the advertising keywords, meta title and description, and body of an lp, like in figure 1 , are standard inputs in ad text generation and important for practical use, the introduced rl-based approaches focus on inserting advertising keywords into ad texts. considering lps themselves are written by professional ad creators and enriched more compared with advertising keywords, lps have the potential to contribute to generating relevant and attractive ad texts. in this work, we propose a method to facilitate a model to reuse expressions in lp texts by considering coverage of lp texts as rewards in rl. figure 2 shows the desired ad text in our proposed method. as shown in the figure, reusing expressions in lp texts has the potential to improve relevance and attractiveness to lp texts in ad text generation. to use our proposed rewards with the conventional to appropriately use multiple rewards in rl for ad text generation, we also explore the usage of their effective combination in ad text generation by rl. we focus on the scaling of each reward as a solution and reveal that scaling is important to improve the coverage of lp texts. furthermore, we conducted automatic and human evaluations on our created ad text generation dataset with incorporating our rewards into t5, a pre-trained transformer. experimental results show that considering our proposed rewards increases lp text coverage in the test set, even compared with a large language model (llm), llama-2. furthermore, our proposed method outperformed human-created reference of descriptions for ad texts in the attractiveness of human evaluation. these results indicate that lp information can be a crucial reward with its appropriate usage and scaling, even when used with other important information like advertising keywords and knowledge in a pre-trained language model. ## our ad text generation method figure 3 shows the overview of our ad text generation. the procedures of the generation process are as follows: transformer (t5) rewards of reinforcement learning (rl) input generate input maximum likelihood estimation (mle) reinforcement learning (rl) landing page (lp) ① ① ② ② ③ ③ meta title & description (mtd) body advertising keyword (kw) sampled title of ad texts decoded title of ad texts sampled descriptions of ad texts decoded descriptions of ad texts reference titles of ad texts reference descriptions of ad texts source and references sampled and decoded texts we explain the details of each part in the following subsections. ## conclusion in this paper, we propose a method to facilitate ad text generation models to use keywords in lp texts through word coverage-based rewards in rl. furthermore, to handle multiple rewards for ad text generation, we introduce scaling of rewards into the ad text generation task. moreover, we evaluated effective combinations of advertising keywords, meta title and description, and body of an lp as rewards in ad text generation by rl. through the evaluation of automatic and human evaluations, we revealed the importance of considering keywords in lp texts and scaling to the combined rewards to improve the performance of generated descriptions for ad texts. in our future work, we plan to apply the rlbased approaches investigated in this work to llms.