Exploring the Relationship Between Diversity and Quality in Ad Text Generation
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Abstract (arXiv)
In natural language generation for advertising, creating diverse and engaging ad texts is crucial for capturing a broad audience and avoiding advertising fatigue. Regardless of the importance of diversity, the impact of the diversity-enhancing methods in ad text generation -- mainly tested on tasks such as summarization and machine translation -- has not been thoroughly explored. Ad text generation significantly differs from these tasks owing to the text style and requirements. This research explores the relationship between diversity and ad quality in ad text generation by considering multiple factors, such as diversity-enhancing methods, their hyperparameters, input-output formats, and the models.
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References · 13
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2019The Curious Case of Neural Text Degenerationcited by 2,916
- 2018Hierarchical Neural Story Generationcited by 1,193
- 2017Six Challenges for Neural Machine Translationcited by 335
- 2024Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilitiescited by 111
- 2022On Decoding Strategies for Neural Text Generatorscited by 90
- 2016A Simple, Fast Diverse Decoding Algorithm for Neural Generationcited by 58
- 2023Striking Gold in Advertising: Standardization and Exploration of Ad Text Generationcited by 17
- 2024Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decodingcited by 11
- 2022DeepGen: Diverse Search Ad Generation and Real-Time Customizationcited by 10
- 2023Natural Language Generation for Advertising: A Surveycited by 10
- 2025AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Textscited by 5
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