mlsanityData to 14 Oct 2026

Natural Language Generation for Advertising: A Survey

arXiv 2306.12719 ↗First posted 22 Jun 2023cs.CLcited by 10

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Abstract (arXiv)

Natural language generation methods have emerged as effective tools to help advertisers increase the number of online advertisements they produce. This survey entails a review of the research trends on this topic over the past decade, from template-based to extractive and abstractive approaches using neural networks. Additionally, key challenges and directions revealed through the survey, including metric optimization, faithfulness, diversity, multimodality, and the development of benchmark datasets, are discussed.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 25 of its 60 references that are in the map. First posted on arXiv 22 Jun 2023.

Builds on

25 of 60 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,361
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  3. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  4. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,046
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  11. 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 155
  12. 2020Evaluating the Evaluation of Diversity in Natural Language Generationcited by 129
13 more
  1. 2021Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarizationcited by 129
  2. 2019Mixture Models for Diverse Machine Translation: Tricks of the Tradecited by 77
  3. 2017Optimized Cost per Click in Taobao Display Advertisingcited by 57
  4. 2020Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinementcited by 29
  5. 2022CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuningcited by 17
  6. 2021Controllable and Diverse Text Generation in E-commercecited by 13
  7. 2022DeepGen: Diverse Search Ad Generation and Real-Time Customizationcited by 10
  8. 2022FAST: Improving Controllability for Text Generation with Feedback Aware Self-Trainingcited by 7
  9. 2019Algorithmic Copywriting: Automated Generation of Health-Related Advertisements to Improve their Performancecited by 5
  10. 2020Query-Variant Advertisement Text Generation with Association Knowledgecited by 5
  11. 2021TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similaritycited by 4
  12. 2021Toward Improving Coherence and Diversity of Slogan Generationcited by 1
  13. 2022Effective and Efficient Query-aware Snippet Extraction for Web Searchcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 3; 2026: 2.

10 papers.

  1. 2023A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directionscited by 105
  2. 2024DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarioscited by 38
  3. 2023Striking Gold in Advertising: Standardization and Exploration of Ad Text Generationcited by 17
  4. 2025AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Textscited by 5
  5. 2024AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertisingcited by 4
  6. 2024Cross-cultural Inspiration Detection and Analysis in Real and LLM-generated Social Media Datacited by 3
  7. 2025AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Datasetcited by 2
  8. 2026RELATE: A Reinforcement Learning-Enhanced LLM Framework for Advertising Text Generationcited by 2
  9. 2025Exploring the Relationship Between Diversity and Quality in Ad Text Generationcited by 0
  10. 2026Recontextualizing Famous Quotes for Brand Slogan Generationcited by 0