Detecting Contextomized Quotes in News Headlines by Contrastive Learning
Seonyeong Song, Hyeonho Song, Kunwoo Park, Jiyoung Han, Meeyoung Cha
Abstract
Quotes are critical for establishing credibility in news articles. A direct quote enclosed in quotation marks has a strong visual appeal and is a sign of a reliable citation. Unfortunately, this journalistic practice is not strictly followed, and a quote in the headline is often “contextomized.” Such a quote uses words out of context in a way that alters the speaker’s intention so that there is no semantically matching quote in the body text. We present QuoteCSE, a contrastive learning framework that represents the embedding of news quotes based on domain-driven positive and negative samples to identify such an editorial strategy. The dataset and code are available at https://github.com/ssu-humane/contextomized-quote-contrastive.- Anthology ID:
- 2023.findings-eacl.52
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2023
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Andreas Vlachos, Isabelle Augenstein
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 697–704
- Language:
- URL:
- https://aclanthology.org/2023.findings-eacl.52
- DOI:
- 10.18653/v1/2023.findings-eacl.52
- Cite (ACL):
- Seonyeong Song, Hyeonho Song, Kunwoo Park, Jiyoung Han, and Meeyoung Cha. 2023. Detecting Contextomized Quotes in News Headlines by Contrastive Learning. In Findings of the Association for Computational Linguistics: EACL 2023, pages 697–704, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Contextomized Quotes in News Headlines by Contrastive Learning (Song et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.findings-eacl.52.pdf