Detecting Contextomized Quotes in News Headlines by Contrastive Learning

• URL: http://arxiv.org/abs/2302.04465v1
• Date: Thu, 9 Feb 2023 07:04:11 GMT
• Title: Detecting Contextomized Quotes in News Headlines by Contrastive Learning
• Abstract summary: 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.
• Score: 9.90130000354593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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