Challenge: Existing datasets address specific humor templates, such as funny one-liners and filling in Mad Libs R.
Approach: They introduce a dataset for research in computational humor that uses crowdsourced editing techniques to create funny headlines.
Outcome: The new dataset supports classic theories of humor, including incongruity, superiority, setup/punchline.

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Stimulating Creativity with FunLines: A Case Study of Humor Generation in Headlines (2020.acl-demos)

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Challenge: FunLines is an online game that allows players to generate and rate funny news headlines . it is difficult to generate data that depends on human creativity, and measuring creativity often requires more effort.
Approach: They propose a game where players edit news headlines to make them funny and rate the funniness of headlines edited by others.
Outcome: The proposed game outperforms other crowdsourcing approaches in generating humor datasets.
Making People Laugh like a Pro: Analysing Humor Through Stand-Up Comedy (2022.lrec-1)

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Challenge: a lot of computational tools focus on standalone jokes or on occasional humorous sentences during presentations.
Approach: They propose to use stand-up comedy transcripts to extract humor from a larger narrative.
Outcome: The dataset, SCRIPTS, is built using stand-up comedy shows transcripts.
HAHA 2019 Dataset: A Corpus for Humor Analysis in Spanish (2020.lrec-1)

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Challenge: 30,000 Spanish tweets were crowd-annotated with humor value and funniness score . the corpus contains approximately 38.6% of humorous tweets with an average score of 2.04 in a scale from 1 to 5 for the humorous tweet.
Approach: They develop a corpus of 30,000 Spanish tweets crowd-annotated with humor value and funniness score.
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Hooks in the Headline: Learning to Generate Headlines with Controlled Styles (2020.acl-main)

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Challenge: Current summarization systems only produce plain, factual headlines, far from the practical needs for exposure and memorableness of the articles.
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Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes (D19-1)

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Challenge: Humor plays important role in human communication, which makes it important problem for natural language processing.
Approach: They propose a novel annotation scheme to give scenarios of how humor arises in text . they report reasonable agreement between annotators and analyze the dataset .
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A Case Study on Neural Headline Generation for Editing Support (N19-2)

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Challenge: a news-aggregator is a website or mobile application that aggregates web content . dozens of professional editors manually create their headlines, which are much shorter than the original headlines.
Approach: They propose a neural headline generation model that automatically generates short headlines from news articles.
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The rJokes Dataset: a Large Scale Humor Collection (2020.lrec-1)

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Challenge: Humor is a complex language phenomenon that depends upon many factors, including topic, date, and recipient.
Approach: They compile a large scale humor dataset from the Reddit r/Jokes subreddit.
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How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements (2021.acl-long)

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Challenge: Humor is an important social phenomenon, serving complex social and psychological functions.
Approach: They propose a novel algorithm for automatically detecting funny scientific papers . they use a dataset containing thousands of funny papers to learn classifiers .
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BREAKING! Presenting Fake News Corpus for Automated Fact Checking (P19-2)

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Challenge: a new study shows that fake news spreads faster than mainstream articles on the same topic . however, there is no dataset containing compelling fake and questionable news articles .
Approach: They introduce manually verified corpus of compelling fake and questionable news articles on the USA politics . they plan to extend the corpus in the future and use it for automated fake news detection.
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Updated Headline Generation: Creating Updated Summaries for Evolving News Stories (2022.acl-long)

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Challenge: Existing systems that generate headlines for updated articles are not as efficient as static ones.
Approach: They propose a task where a system generates a headline for an updated article, considering both the previous article and headline.
Outcome: The proposed model produces headlines judged by humans to be as factual as gold headlines while making fewer unnecessary edits compared to a standard headline generation model.

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