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

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