Context-Driven Satirical News Generation (2020)

Abstract: While mysterious, humor likely hinges on an interplay of entities, their relationships, and cultural connotations. Motivated by the importance of context in humor, we consider methods for constructing and leveraging contextual representations in generating humorous text. Specifically, we study the capacity of transformer-based architectures to generate funny satirical headlines, and show that both language models and summarization models can be fine-tuned to regularly generate headlines that people find funny. Furthermore, we find that summarization models uniquely support satire-generation by enabling the generation of topical humorous text. Outside of our formal study, we note that headlines generated by our model were accepted via a competitive process into a satirical newspaper, and one headline was ranked as high or better than 73% of human submissions. As part of our work, we contribute a dataset of over 15K satirical headlines paired with ranked contextual information from news articles and Wikipedia.

## introduction despite long-term interest in the foundations of humor, work to date in the nlp community on humorous text has largely relied on surface-level features (e.g., puns). we study employing richer contextual representations to generate satirical news headlines, which necessitate a balance between funniness and topicality. while our particular focus is humor, our methods are broadly applicable to tasks that require reasoning over textual knowledge. consider the following satirical headline from the onion (theonion.com): ## our approach we now provide background on our methods. ## experimental design we tested our models by sampling headline generations and evaluating their quality via crowdsourcing. ## results this section describe the results of our evaluation. 2 we excluded the model trained on the unfun.me corpus, as only 2.5% of its generations were rated as funny, and 64% of generations were simply duplicates of the original input context. the redundant generations likely resulted from the small corpus size, and the significant word-overlap between the 'unfunned' input and labels. ## future work we intend to further investigate the latent and embedding spaces of our model, and hope to better elucidate the neural-logic that transposes everyday events into humorous language. additionally, our context-driven approach allows us to examine the relationship between real-world, input events, and the resulting satirical output. we plan to continue probing this relationship, and to refine our understanding of the processes underlying our generations, and how their relationship to realworld events can be interpreted within proposed theories of humor, including the script-based semantic theory of humor (see a.4). while we treat document retrieval as a prepossessing step, we can also explore including retrieval in end-to-end training, as employed by @xcite . lastly, we are fascinated by the potential for leveraging other text-based contextual representations, ranging from alternative types of document and longer-form text, to graph-encoded representations of events and entities. these approaches can provide alternative blends of depth, concision, and structure. ## conclusion we introduced a methodology and formalization for modeling satirical news headlines as conditioned on a real-world context, and presented an information retrieval pipeline for constructing that context. we found that pretrained abstractive summarization models provide powerful feature extractors atop these rich contextual representations. conditioning on such contextual documents enabled the generation of topical satire that people determined to be funnier than headlines generated via context-free methods. additionally, we discovered that the approach generalizes to new topics and circumstances. moving beyond the focus on generating satirical headlines, we believe that variations of our approach are broadly applicable to tasks ranging from information retrieval and dialogue to reading comprehension. our work provides further evidence that neural architectures, augmented with task-relevant contextual information, have the potential to reason about sub-textual concepts, including the subtleties of humor. zhiwei yu, jiwei tan, and xiaojun wan. 2018. a neural approach to pun generation. in proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: long papers), pages 1650-1660, melbourne, australia. association for computational linguistics.