Abstract
The task of clickbait spoiling is: generating a short text that satisfies the curiosity induced by a clickbait post. Clickbait links to a web page and advertises its contents by arousing curiosity instead of providing an informative summary. Previous studies on clickbait spoiling has shown the approach that classifing the type of spoilers is needed, then generating the appropriate spoilers is more effective on the Webis Clickbait Spoiling Corpus 2022 dataset. Our contribution focused on study of the three classes (phrase, passage and multi) and finding appropriate models to generate spoilers foreach class. Results were analysed in each type of spoilers, revealed some reasons of having diversed results in different spoiler types. “passage” type spoiler was identified as the most difficult and the most valuable type of spoiler.- Anthology ID:
- 2023.semeval-1.8
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 64–76
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.8
- DOI:
- 10.18653/v1/2023.semeval-1.8
- Cite (ACL):
- Shirui Tang. 2023. Brooke-English at SemEval-2023 Task 5: Clickbait Spoiling. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 64–76, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Brooke-English at SemEval-2023 Task 5: Clickbait Spoiling (Tang, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.semeval-1.8.pdf