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.
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CITATION STYLE
Song, S., Song, H., Park, K., Han, J., & Cha, M. (2023). Detecting Contextomized Quotes in News Headlines by Contrastive Learning. In EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023 (pp. 685–692). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.findings-eacl.52
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