The New Yorker publishes a weekly captionless cartoon. More than 5,000
readers submit captions for it. The editors select three of them and ask the
readers to pick the funniest one. We describe an experiment that compares a
dozen automatic methods for selecting the funniest caption. We show that
negative sentiment, human-centeredness, and lexical centrality most strongly
match the funniest captions, followed by positive sentiment. These results are
useful for understanding humor and also in the design of more engaging
conversational agents in text and multimodal (vision+text) systems. As part of
this work, a large set of cartoons and captions is being made available to the
community.

本研究实验比较了十二种自动方法来选择最有趣的标题，结果表明，负面情感、以人为本和词汇中心性最能匹配最有趣的标题，其次是正面情感。这些结果有助于理解幽默，并在文本和多模（视觉 + 文本）系统中设计更加引人入胜的对话代理人。