# Exploring the Relationship Between Diversity and Quality in Ad Text Generation

arXiv: https://arxiv.org/abs/2505.16418 · HTML: https://mlsanity.com/paper/2505.16418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 13 of 13)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities](https://mlsanity.com/paper/2404.17790/) (2024)
- [On Decoding Strategies for Neural Text Generators](https://mlsanity.com/paper/2203.15721/) (2022)
- [A Simple, Fast Diverse Decoding Algorithm for Neural Generation](https://mlsanity.com/paper/1611.08562/) (2016)
- [Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation](https://mlsanity.com/paper/2309.12030/) (2023)
- [Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2401.05054/) (2024)
- [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)
- [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)

## 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.