@inproceedings{sterz-etal-2023-ml,
title = "{ML} Mob at {S}em{E}val-2023 Task 5: {``}Breaking News: Our Semi-Supervised and Multi-Task Learning Approach Spoils Clickbait{''}",
author = "Sterz, Hannah and
Bongard, Leonard and
Werner, Tobias and
Poth, Clifton and
Hentschel, Martin",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.251",
doi = "10.18653/v1/2023.semeval-1.251",
pages = "1818--1823",
abstract = "Online articles using striking headlines that promise intriguing information are often used to attract readers. Most of the time, the information provided in the text is disappointing to the reader after the headline promised exciting news. As part of the SemEval-2023 challenge, we propose a system to generate a spoiler for these headlines. The spoiler provides the information promised by the headline and eliminates the need to read the full article. We consider Multi-Task Learning and generating more data using a distillation approach in our system. With this, we achieve an F1 score up to 51.48{\%} on extracting the spoiler from the articles.",
}
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<abstract>Online articles using striking headlines that promise intriguing information are often used to attract readers. Most of the time, the information provided in the text is disappointing to the reader after the headline promised exciting news. As part of the SemEval-2023 challenge, we propose a system to generate a spoiler for these headlines. The spoiler provides the information promised by the headline and eliminates the need to read the full article. We consider Multi-Task Learning and generating more data using a distillation approach in our system. With this, we achieve an F1 score up to 51.48% on extracting the spoiler from the articles.</abstract>
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%0 Conference Proceedings
%T ML Mob at SemEval-2023 Task 5: “Breaking News: Our Semi-Supervised and Multi-Task Learning Approach Spoils Clickbait”
%A Sterz, Hannah
%A Bongard, Leonard
%A Werner, Tobias
%A Poth, Clifton
%A Hentschel, Martin
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sterz-etal-2023-ml
%X Online articles using striking headlines that promise intriguing information are often used to attract readers. Most of the time, the information provided in the text is disappointing to the reader after the headline promised exciting news. As part of the SemEval-2023 challenge, we propose a system to generate a spoiler for these headlines. The spoiler provides the information promised by the headline and eliminates the need to read the full article. We consider Multi-Task Learning and generating more data using a distillation approach in our system. With this, we achieve an F1 score up to 51.48% on extracting the spoiler from the articles.
%R 10.18653/v1/2023.semeval-1.251
%U https://aclanthology.org/2023.semeval-1.251
%U https://doi.org/10.18653/v1/2023.semeval-1.251
%P 1818-1823
Markdown (Informal)
[ML Mob at SemEval-2023 Task 5: “Breaking News: Our Semi-Supervised and Multi-Task Learning Approach Spoils Clickbait”](https://aclanthology.org/2023.semeval-1.251) (Sterz et al., SemEval 2023)
ACL