Summary of Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning, by Sayantan Pal et al.
Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning
by Sayantan Pal, Souvik Das, Rohini K. Srihari
First submitted to arxiv on: 7 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study introduces a novel technique called ‘clickbait spoiling’, which detects, categorizes, and generates succinct text responses to counter curiosity induced by clickbait content. The multi-task learning framework enhances generalization capabilities, addressing the pervasive issue of clickbait. The research focuses on generating appropriate spoilers in various forms, including phrases, passages, or multiple texts. A refined spoiler categorization method and a modified QA mechanism are integrated within a multi-task learning paradigm for optimized spoiler extraction from context. Fine-tuning methods accommodate longer sequence handling for extended spoiler generation. This study highlights the potential of sophisticated text processing techniques to tackle clickbait, promising an enhanced user experience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about a new way to detect and respond to ‘clickbait’ online content that tries to get your attention by being mysterious or intriguing. The researchers created a system called ‘clickbait spoiling’ that can generate short answers to the questions raised by clickbait, so people don’t feel left out or confused. They used a special way of training their model that lets it learn from multiple tasks at once, which helped improve its performance. This research shows how advanced text processing techniques can help make online interactions more enjoyable and less frustrating. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Generalization » Multi task
