Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning (D19-1)
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| Challenge: | Conventional abstractive headline generation methods do not optimize for maximum reader attention. |
| Approach: | They propose a model that generates sensational headlines without labeled data by classifying online headlines with many comments against a summarization model. |
| Outcome: | The proposed model generates sensational headlines without labeled data. |
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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. |
| Approach: | They propose a task to generate relevant headlines with three style options . they propose combining summarization and reconstruction tasks into a multitasking framework . |
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Ad Headline Generation using Self-Critical Masked Language Model (2021.naacl-industry)
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| Challenge: | We propose a programmatic solution to generate product advertising headlines using retail content. |
| Approach: | They propose a programmatic solution to generate product advertising headlines using retail content . they use Reinforcement Learning (RL) Policy gradient methods on Transformer . |
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Contrastive Learning enhanced Author-Style Headline Generation (2022.emnlp-main)
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| Challenge: | Current work only uses the article itself in the headline generation, but have not taken the writing style of headlines into account. |
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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
| 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. |
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Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning (2022.aacl-main)
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| Challenge: | Recent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive and would be small-scale. |
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Know Better – A Clickbait Resolving Challenge (2022.lrec-1)
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| Challenge: | a clickbait headline or teaser is used to "bait" the reader into clicking a link to an article . clickbaiting is annoying but effective, and can be countered with specialized models . |
| Approach: | They propose to construct approaches that can automatically extract relevant information from clickbait articles . they argue that clickbaiting can probably not be defeated with clickbaitting detection alone . |
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Generating User-Engaging News Headlines (2023.acl-long)
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| Challenge: | Personalized news recommendation systems present the same headline to all users, making it difficult for them to understand the connection between their interests and the recommended article. |
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HonestBait: Forward References for Attractive but Faithful Headline Generation (2023.findings-acl)
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| Challenge: | Current approaches to generating attractive headlines often learn directly from data based on clicks and views . clickbait models fail to reveal how much interest is raised by the writing style and how much is due to the event or topic itself . |
| Approach: | They propose a framework for generating headlines using forward references . they use a dataset containing pairs of fake news and verified news . |
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MediaHG: Rethinking Eye-catchy Features in Social Media Headline Generation (2023.emnlp-main)
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| Challenge: | Creating a good headline on social media platforms requires a disentanglement-based model to balance the content and contextual features. |
| Approach: | They propose a disentanglement-based headline generation model which can balance the content and contextual features by incorporating contrastive learning and auxiliary multi-tasking to choose the best domain-suitable headline. |
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NumHG: A Dataset for Number-Focused Headline Generation (2024.lrec-main)
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| Challenge: | a lack of fine-grained annotations for accurate numeral generation in headlines is a major roadblock . a new dataset, the NumHG, provides over 27,000 annotated numeral-rich news articles for detailed investigation . |
| Approach: | They propose a dataset that provides annotated numerals for headline generation . they evaluate five well-performing headline-generation models using human evaluation . |
| Outcome: | The proposed dataset provides annotated numeral-rich news articles for detailed investigation. |