@inproceedings{song-etal-2023-detecting,
title = "Detecting Contextomized Quotes in News Headlines by Contrastive Learning",
author = "Song, Seonyeong and
Song, Hyeonho and
Park, Kunwoo and
Han, Jiyoung and
Cha, Meeyoung",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.52",
doi = "10.18653/v1/2023.findings-eacl.52",
pages = "697--704",
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 \url{https://github.com/ssu-humane/contextomized-quote-contrastive}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Detecting Contextomized Quotes in News Headlines by Contrastive Learning
%A Song, Seonyeong
%A Song, Hyeonho
%A Park, Kunwoo
%A Han, Jiyoung
%A Cha, Meeyoung
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F song-etal-2023-detecting
%X 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.
%R 10.18653/v1/2023.findings-eacl.52
%U https://aclanthology.org/2023.findings-eacl.52
%U https://doi.org/10.18653/v1/2023.findings-eacl.52
%P 697-704
Markdown (Informal)
[Detecting Contextomized Quotes in News Headlines by Contrastive Learning](https://aclanthology.org/2023.findings-eacl.52) (Song et al., Findings 2023)
ACL