A d P araphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset (2025)

Abstract: Identifying factors that make ad text attractive is essential for advertising success. This study proposes AdParaphrase v2.0, a dataset for ad text paraphrasing, containing human preference data, to enable the analysis of the linguistic factors and to support the development of methods for generating attractive ad texts. Compared with v1.0, this dataset is 20 times larger, comprising 16,460 ad text paraphrase pairs, each annotated with preference data from ten evaluators, thereby enabling a more comprehensive and reliable analysis. Through the experiments, we identified multiple linguistic features of engaging ad texts that were not observed in v1.0 and explored various methods for generating attractive ad texts. Furthermore, our analysis demonstrated the relationships between human preference and ad performance, and highlighted the potential of reference-free metrics based on large language models for evaluating ad text attractiveness.The dataset is publicly available at: https://github.com/CyberAgentAILab/AdParaphrase-v2.0.

## introduction advertisements play a vital role in marketing, raising awareness of products or services, capturing user interests, and driving actions such as clicks. to maximize their effectiveness, ad writers must create attractive ad texts that appeal to users. however, with the growing demand for online advertising, manual ad text creation is reaching practical limitations, highlighting the need for automatic ad text generation (atg) @xcite . writing attractive ad texts requires considering two aspects: what-to-say (the content to be advertised, such as price or product name) and how-to-say (the way the content is expressed). this study focuses on the how-to-say aspect in exploring methods for generating attractive ad texts, aiming to identify linguistic factors that capture the user's interest. many studies have investigated the factors that influence ad performance and human preference @xcite . however, identifying the linguistic factors presents a significant challenge because of the intricate interplay between the semantic content and its linguistic expression. a clear analysis of the linguistic factors requires disentangling them and focusing exclusively on their impact @xcite . to address this challenge, @xcite introduced adparaphrase, which is a dataset comprising paraphrase pairs of ad texts, annotated with human preferences from ten evaluators. by controlling the content, the dataset allows us to investigate how linguistic expressions alone affect the attractiveness of the ad. using this dataset, they identified linguistic factors, such as noun count, that significantly affect human preferences. in addition, they demonstrated that these findings can improve the generation of attractive ad texts. however, the small size of their dataset, ad-paraphrase, presents notable limitations. the dataset contains only 725 paraphrase pairs created by professional ad writers and is insufficient for conducting comprehensive and reliable analyses or training atg models. consequently, previous studies have primarily relied on in-context learning (icl) @xcite , leaving other promising approaches, such as preference tuning @xcite , unexplored. to address these limitations, we present ad-paraphrase v2.0, an expanded version of the original dataset, with referring to the original dataset as v1.0. in the experiments, we analyzed adpara-phrase v2.0 and identified multiple linguistic factors influencing human preferences that were not identified in v1.0 ( §5.1). we then evaluated various methods for generating attractive ad texts, including icl, instruction tuning, and preference tuning, by examining the characteristics of each approach ( §5.2). in addition, our analysis identified the relationships between human preferences and ad performances, and demonstrated the suitability of reference-free metrics for the automatic evaluation of ad text attractiveness ( §6). we hope adparaphrase v2.0 will drive further advancements in generating attractive ad texts. ## method of dataset construction this section describes the design principles of adparaphrase v2.0 ( §3.1), the three-step construction process involving paraphrase candidate collection ( §3.2), paraphrase identification ( §3.3), and preference judgment ( §3.4), and the quality control measures implemented throughout its construction ( §3.5). ## analysis in this section, we report on the analyses conducted from two main perspectives, to contribute to the future development of attractive ad text generation. the first is an analysis of the relationship between human preferences and ad performance. given that the ultimate goal of advertising is to optimize ad performance (e.g., clicks), clarifying the relationship between ad text preferences and ad performance is crucial. the second perspective concerns automatic evaluation of pi and attractiveness. although pi and attractiveness were evaluated manually in this study, verifying automatic evaluation metrics as alternatives to manual evaluation is required to enhance efficiency in future research. for the former perspective, we conducted two experiments: evaluating the relationship between human preference and predicted ctr (pctr) ( §6.1) and assessing ad performance in a real-world environment online ( §6.2). for the latter, a metaevaluation was performed to assess the relationship between human evaluation and existing automatic evaluation metrics ( §6.3). bold values indicate statistically significant differences, as determined by a z-test (@xmath0.01). ## conclusion this study introduced adparaphrase v2.0, a dataset for ad text paraphrasing that contains human preference data. compared to v1.0, our dataset is 20 times larger, enabling a comprehensive analysis of the key features that make ad text attractive. we identified multiple linguistic features that contribute to engaging ad texts and investigated various methods for generating attractive ad texts. our analysis revealed the relationship between human preference and ad performance, and demonstrated the potential of reference-free evaluation for assessing ad text attractiveness. future work will include enhancing atg methods by addressing challenges such as adhering to length constraints, optimizing both human preference and ad performance, and investigating the influence of other factors on preferences, such as demographic information and product category.