# Natural Language Generation for Advertising: A Survey

arXiv: https://arxiv.org/abs/2306.12719 · HTML: https://mlsanity.com/paper/2306.12719/

## 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 (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018)
- [SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization](https://mlsanity.com/paper/2111.09525/) (2021)
- [Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text](https://mlsanity.com/paper/2202.06935/) (2022)
- [Evaluating the Evaluation of Diversity in Natural Language Generation](https://mlsanity.com/paper/2004.02990/) (2020)
- [Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization](https://mlsanity.com/paper/2109.09784/) (2021)
- [Mixture Models for Diverse Machine Translation: Tricks of the Trade](https://mlsanity.com/paper/1902.07816/) (2019)
- [Optimized Cost per Click in Taobao Display Advertising](https://mlsanity.com/paper/1703.02091/) (2017)
- [Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement](https://mlsanity.com/paper/2008.07467/) (2020)
- [CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning](https://mlsanity.com/paper/2205.08943/) (2022)
- [Controllable and Diverse Text Generation in E-commerce](https://mlsanity.com/paper/2102.11497/) (2021)
- [DeepGen: Diverse Search Ad Generation and Real-Time Customization](https://mlsanity.com/paper/2208.03438/) (2022)
- [FAST: Improving Controllability for Text Generation with Feedback Aware Self-Training](https://mlsanity.com/paper/2210.03167/) (2022)
- [Algorithmic Copywriting: Automated Generation of Health-Related Advertisements to Improve their Performance](https://mlsanity.com/paper/1910.12274/) (2019)
- [Query-Variant Advertisement Text Generation with Association Knowledge](https://mlsanity.com/paper/2004.06438/) (2020)
- [TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity](https://mlsanity.com/paper/2108.08226/) (2021)
- [Toward Improving Coherence and Diversity of Slogan Generation](https://mlsanity.com/paper/2102.05924/) (2021)
- [Effective and Efficient Query-aware Snippet Extraction for Web Search](https://mlsanity.com/paper/2210.08809/) (2022)

## Built on by (most cited first, 10 of 10)

- [A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://mlsanity.com/paper/2310.14724/) (2023)
- [DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios](https://mlsanity.com/paper/2410.23746/) (2024)
- [Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation](https://mlsanity.com/paper/2309.12030/) (2023)
- [AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Texts](https://mlsanity.com/paper/2502.04674/) (2025)
- [AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising](https://mlsanity.com/paper/2408.05906/) (2024)
- [Cross-cultural Inspiration Detection and Analysis in Real and LLM-generated Social Media Data](https://mlsanity.com/paper/2404.12933/) (2024)
- [AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset](https://mlsanity.com/paper/2505.20826/) (2025)
- [RELATE: A Reinforcement Learning-Enhanced LLM Framework for Advertising Text Generation](https://mlsanity.com/paper/2602.11780/) (2026)
- [Exploring the Relationship Between Diversity and Quality in Ad Text Generation](https://mlsanity.com/paper/2505.16418/) (2025)
- [Recontextualizing Famous Quotes for Brand Slogan Generation](https://mlsanity.com/paper/2602.06049/) (2026)

## 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.