Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913344031555584 |
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| author | Pal, Sayantan Das, Souvik Srihari, Rohini K. |
| author_facet | Pal, Sayantan Das, Souvik Srihari, Rohini K. |
| contents | This study introduces 'clickbait spoiling', a novel technique designed to detect, categorize, and generate spoilers as succinct text responses, countering the curiosity induced by clickbait content. By leveraging a multi-task learning framework, our model's generalization capabilities are significantly enhanced, effectively addressing the pervasive issue of clickbait. The crux of our research lies in generating appropriate spoilers, be it a phrase, an extended passage, or multiple, depending on the spoiler type required. Our methodology integrates two crucial techniques: a refined spoiler categorization method and a modified version of the Question Answering (QA) mechanism, incorporated within a multi-task learning paradigm for optimized spoiler extraction from context. Notably, we have included fine-tuning methods for models capable of handling longer sequences to accommodate the generation of extended spoilers. This research highlights the potential of sophisticated text processing techniques in tackling the omnipresent issue of clickbait, promising an enhanced user experience in the digital realm. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_04292 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning Pal, Sayantan Das, Souvik Srihari, Rohini K. Computation and Language Artificial Intelligence This study introduces 'clickbait spoiling', a novel technique designed to detect, categorize, and generate spoilers as succinct text responses, countering the curiosity induced by clickbait content. By leveraging a multi-task learning framework, our model's generalization capabilities are significantly enhanced, effectively addressing the pervasive issue of clickbait. The crux of our research lies in generating appropriate spoilers, be it a phrase, an extended passage, or multiple, depending on the spoiler type required. Our methodology integrates two crucial techniques: a refined spoiler categorization method and a modified version of the Question Answering (QA) mechanism, incorporated within a multi-task learning paradigm for optimized spoiler extraction from context. Notably, we have included fine-tuning methods for models capable of handling longer sequences to accommodate the generation of extended spoilers. This research highlights the potential of sophisticated text processing techniques in tackling the omnipresent issue of clickbait, promising an enhanced user experience in the digital realm. |
| title | Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2405.04292 |