| Challenge: | A teaser is a short reading suggestion for an article that is illustrative and includes curiosity-arousing elements to entice potential readers to read particular news items. |
| Approach: | They propose a benchmark and baseline system for the process of generating teasers. |
| Outcome: | The proposed system is best performing with a pointer network. |
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| Challenge: | Pun generation aims to modify linguistic elements in text to produce humour or evoke double meanings. |
| Approach: | They propose to review pun generation datasets and methods across different stages . pun generation aims to produce humour or evoke double meanings . |
| Outcome: | This paper summarises both automated and human evaluation metrics used to assess the quality of pun generation. |
A Case Study on Neural Headline Generation for Editing Support (N19-2)
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Kazuma Murao, Ken Kobayashi, Hayato Kobayashi, Taichi Yatsuka, Takeshi Masuyama, Tatsuru Higurashi, Yoshimune Tabuchi
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Hierarchical Neural Story Generation (P18-1)
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| Challenge: | a hierarchical model that generates a premise and then conditions on it creates fluent text . a novel form of model fusion improves the relevance of the story to the prompt . |
| Approach: | They use a hierarchical model that first generates a premise, then transforms it into a text . they use fusion to improve relevance of the story to the prompt and add a gated mechanism to model context . |
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What’s The Latest? A Question-driven News Chatbot (2020.acl-demos)
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| Challenge: | a news chatbot that draws content from news articles creates conversations with a user about the news. |
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Paraphrase Generation: A Survey of the State of the Art (2021.emnlp-main)
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| Challenge: | Using neural models, paraphrase generation research has shifted to neural methods . a recent study focused on paraphrases, which are used in language understanding tasks . |
| Approach: | They propose to use neural methods to generate fluent, diverse paraphrases from a sentence . they propose to combine large pretrained language models with other mechanisms to generate more advanced paraphrase generation models. |
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Threat Scenarios and Best Practices to Detect Neural Fake News (2022.coling-1)
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| Challenge: | During the COVID-19 pandemic, inaccurate information made it hard for people to find reliable guidance when they needed it. |
| Approach: | They propose to use pretrained language models to generate fluent, original text . they argue that strong detectors should be released along with new generators . |
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CONSISTENT: Open-Ended Question Generation From News Articles (2022.findings-emnlp)
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| Challenge: | Recent work on question generation has largely focused on factoid questions such as who, what, where, when about basic facts. |
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Demystifying Neural Fake News via Linguistic Feature-Based Interpretation (2022.coling-1)
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| Challenge: | Recent advances to neural fake news generators have made it difficult to understand how misinformation generated by these models may best be confronted. |
| Approach: | They conduct feature-based analysis to gain an interpretative understanding of the linguistic attributes that neural fake news generators may most effectively exploit. |
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Neural Caption Generation for News Images (L18-1)
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| Challenge: | Existing methods for automatic caption generation of images are lacking in the field of image-related applications. |
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Cue Me In: Content-Inducing Approaches to Interactive Story Generation (2020.aacl-main)
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| Challenge: | Existing methods for automatic story generation focus on one-shot generation, but we focus on interactive story generation. |
| Approach: | They propose two ways to incorporate user-provided cue phrases into automatic story generation. |
| Outcome: | The proposed approach produces more topically coherent and personalized stories than baseline methods. |