# Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation

arXiv: https://arxiv.org/abs/2309.12030 · HTML: https://mlsanity.com/paper/2309.12030/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (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)
- [DeepGen: Diverse Search Ad Generation and Real-Time Customization](https://mlsanity.com/paper/2208.03438/) (2022)
- [Natural Language Generation for Advertising: A Survey](https://mlsanity.com/paper/2306.12719/) (2023)
- [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)
- [Aspect-based Analysis of Advertising Appeals for Search Engine Advertising](https://mlsanity.com/paper/2204.11445/) (2022)

## Built on by (most cited first, 17 of 17)

- [Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability](https://mlsanity.com/paper/2506.15629/) (2025)
- [GEM-Bench: A Benchmark for Ad-Injected Response Generation within Generative Engine Marketing](https://mlsanity.com/paper/2509.14221/) (2025)
- [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)
- [Understanding Gender Bias in AI-Generated Product Descriptions](https://mlsanity.com/paper/2506.05390/) (2025)
- [HLLM-Creator: Hierarchical LLM-based Personalized Creative Generation](https://mlsanity.com/paper/2508.18118/) (2025)
- [AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset](https://mlsanity.com/paper/2505.20826/) (2025)
- [NaiAD: Initiate Data-Driven Research for LLM Advertising](https://mlsanity.com/paper/2605.09918/) (2026)
- [TextTIGER: Text-based Intelligent Generation with Entity Prompt Refinement for Text-to-Image Generation](https://mlsanity.com/paper/2504.18269/) (2025)
- [OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent](https://mlsanity.com/paper/2507.02353/) (2025)
- [Generative Modeling with Multi-Instance Reward Learning for E-commerce Creative Optimization](https://mlsanity.com/paper/2508.09730/) (2025)
- [Interactor: Agentic RL oriented Iterative Creation for Ad Description Generation in Sponsored Search](https://mlsanity.com/paper/2606.15911/) (2026)
- [FaithCAMERA: Construction of a Faithful Dataset for Ad Text Generation](https://mlsanity.com/paper/2410.03839/) (2024)
- [Exploring the Relationship Between Diversity and Quality in Ad Text Generation](https://mlsanity.com/paper/2505.16418/) (2025)
- [AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions](https://mlsanity.com/paper/2509.11151/) (2025)
- [Recontextualizing Famous Quotes for Brand Slogan Generation](https://mlsanity.com/paper/2602.06049/) (2026)
- [AdsWorldEngine: A Self-Evolving Conversational Advertising Agent through Orchestrator and Tool Coevolution](https://mlsanity.com/paper/2608.13833/) (2026)

## Abstract (arXiv)

In response to the limitations of manual ad creation, significant research has been conducted in the field of automatic ad text generation (ATG). However, the lack of comprehensive benchmarks and well-defined problem sets has made comparing different methods challenging. To tackle these challenges, we standardize the task of ATG and propose a first benchmark dataset, CAMERA, carefully designed and enabling the utilization of multi-modal information and facilitating industry-wise evaluations. Our extensive experiments with a variety of nine baselines, from classical methods to state-of-the-art models including large language models (LLMs), show the current state and the remaining challenges. We also explore how existing metrics in ATG and an LLM-based evaluator align with human evaluations.