# "President Vows to Cut <Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines

arXiv: https://arxiv.org/abs/1906.00274 · HTML: https://mlsanity.com/paper/1906.00274/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2019.

## Built on by (most cited first, 27 of 27)

- [Goal Driven Discovery of Distributional Differences via Language Descriptions](https://mlsanity.com/paper/2302.14233/) (2023)
- [Can Pre-trained Language Models Understand Chinese Humor?](https://mlsanity.com/paper/2407.04105/) (2024)
- [Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?](https://mlsanity.com/paper/2105.09142/) (2021)
- [Getting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language Models](https://mlsanity.com/paper/2403.00794/) (2024)
- [Operationalizing Complex Causes: A Pragmatic View of Mediation](https://mlsanity.com/paper/2106.05074/) (2021)
- [Creativity in AI: Progresses and Challenges](https://mlsanity.com/paper/2410.17218/) (2024)
- [Systematic Literature Review: Computational Approaches for Humour Style Classification](https://mlsanity.com/paper/2402.01759/) (2024)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and its Intensity](https://mlsanity.com/paper/2211.01889/) (2022)
- [Chumor 2.0: Towards Benchmarking Chinese Humor Understanding](https://mlsanity.com/paper/2412.17729/) (2024)
- [Chumor 1.0: A Truly Funny and Challenging Chinese Humor Understanding Dataset from Ruo Zhi Ba](https://mlsanity.com/paper/2406.12754/) (2024)
- [Deceptive Humor: A Synthetic Multilingual Benchmark Dataset for Bridging Fabricated Claims with Humorous Content](https://mlsanity.com/paper/2503.16031/) (2025)
- [When a Computer Cracks a Joke: Automated Generation of Humorous Headlines](https://mlsanity.com/paper/2109.08702/) (2021)
- [Predicting Through Generation: Why Generation Is Better for Prediction](https://mlsanity.com/paper/2502.17817/) (2025)
- [More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models](https://mlsanity.com/paper/2609.38827/) (2026)
- [A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception](https://mlsanity.com/paper/2208.05757/) (2022)
- [Harm or Humor: A Multimodal, Multilingual Benchmark for Overt and Covert Harmful Humor](https://mlsanity.com/paper/2603.17759/) (2026)
- [Integrating extracted information from bert and multiple embedding methods with the deep neural network for humour detection](https://mlsanity.com/paper/2105.05112/) (2021)
- [kdehumor at semeval-2020 task 7: a neural network model for detecting funniness in dataset humicroedit](https://mlsanity.com/paper/2105.05135/) (2021)
- [From Generalized Laughter to Personalized Chuckles: Unleashing the Power of Data Fusion in Subjective Humor Detection](https://mlsanity.com/paper/2312.11296/) (2023)
- [A Survey of Text Style Transfer: Applications and Ethical Implications](https://mlsanity.com/paper/2407.16737/) (2024)
- [KoWit-24: A Richly Annotated Dataset of Wordplay in News Headlines](https://mlsanity.com/paper/2503.01510/) (2025)
- [FlowNIB: An Information Bottleneck Analysis of Bidirectional vs. Unidirectional Language Models](https://mlsanity.com/paper/2506.00859/) (2025)
- [Decoders Laugh as Loud as Encoders](https://mlsanity.com/paper/2509.04779/) (2025)
- [A Customer Journey in the Land of Oz: Leveraging the Wizard of Oz Technique to Model Emotions in Customer Service Interactions](https://mlsanity.com/paper/2511.21909/) (2025)
- [Learning to Think Like a Cartoon Captionist: Incongruity-Resolution Supervision for Multimodal Humor Understanding](https://mlsanity.com/paper/2604.15210/) (2026)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)

## Abstract (arXiv)

We introduce, release, and analyze a new dataset, called Humicroedit, for research in computational humor. Our publicly available data consists of regular English news headlines paired with versions of the same headlines that contain simple replacement edits designed to make them funny. We carefully curated crowdsourced editors to create funny headlines and judges to score a to a total of 15,095 edited headlines, with five judges per headline. The simple edits, usually just a single word replacement, mean we can apply straightforward analysis techniques to determine what makes our edited headlines humorous. We show how the data support classic theories of humor, such as incongruity, superiority, and setup/punchline. Finally, we develop baseline classifiers that can predict whether or not an edited headline is funny, which is a first step toward automatically generating humorous headlines as an approach to creating topical humor.