# Recontextualizing Famous Quotes for Brand Slogan Generation

arXiv: https://arxiv.org/abs/2602.06049 · HTML: https://mlsanity.com/paper/2602.06049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 19 of 19)

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- [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018)
- [Evaluating the Evaluation of Diversity in Natural Language Generation](https://mlsanity.com/paper/2004.02990/) (2020)
- [Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://mlsanity.com/paper/2306.04140/) (2023)
- [CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning](https://mlsanity.com/paper/2205.08943/) (2022)
- [Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation](https://mlsanity.com/paper/2309.12030/) (2023)
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- [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)
- [Long-Term Ad Memorability: Understanding & Generating Memorable Ads](https://mlsanity.com/paper/2309.00378/) (2023)
- [LLMs for Customized Marketing Content Generation and Evaluation at Scale](https://mlsanity.com/paper/2506.17863/) (2025)
- [Improving Generative Ad Text on Facebook using Reinforcement Learning](https://mlsanity.com/paper/2507.21983/) (2025)
- [CTR-Driven Ad Text Generation via Online Feedback Preference Optimization](https://mlsanity.com/paper/2507.20227/) (2025)
- [Evaluating the Evaluation of Diversity in Commonsense Generation](https://mlsanity.com/paper/2506.00514/) (2025)
- [Beyond Quality: Unlocking Diversity in Ad Headline Generation with Large Language Models](https://mlsanity.com/paper/2508.18739/) (2025)
- [Effective Slogan Generation with Noise Perturbation](https://mlsanity.com/paper/2310.04472/) (2023)

## Abstract (arXiv)

Slogans are concise and memorable catchphrases that play a crucial role in advertising by conveying brand identity and shaping public perception. However, advertising fatigue reduces the effectiveness of repeated slogans, creating a growing demand for novel, creative, and insightful slogan generation. While recent work leverages large language models (LLMs) for this task, existing approaches often produce stylistically redundant outputs that lack a clear brand persona and appear overtly machine-generated. We argue that effective slogans should balance novelty with familiarity and propose a new paradigm that recontextualizes persona-related famous quotes for slogan generation. Well-known quotes naturally align with slogan-length text, employ rich rhetorical devices, and offer depth and insight, making them a powerful resource for creative generation. Technically, we introduce a modular framework that decomposes slogan generation into interpretable subtasks, including quote matching, structural decomposition, vocabulary replacement, and remix generation. Extensive automatic and human evaluations demonstrate marginal improvements in diversity, novelty, emotional impact, and human preference over three state-of-the-art LLM baselines.