Artur Jurk
2022
Clickbait Spoiling via Question Answering and Passage Retrieval
Matthias Hagen
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Maik Fröbe
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Artur Jurk
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Martin Potthast
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
We introduce and study the task of clickbait spoiling: generating a short text that satisfies the curiosity induced by a clickbait post. Clickbait links to a web page and advertises its contents by arousing curiosity instead of providing an informative summary. Our contributions are approaches to classify the type of spoiler needed (i.e., a phrase or a passage), and to generate appropriate spoilers. A large-scale evaluation and error analysis on a new corpus of 5,000 manually spoiled clickbait posts—the Webis Clickbait Spoiling Corpus 2022—shows that our spoiler type classifier achieves an accuracy of 80%, while the question answering model DeBERTa-large outperforms all others in generating spoilers for both types.
2020
1A-Team / Martin-Luther-Universität Halle-Wittenberg@CLSciSumm 20
Artur Jurk
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Maik Boltze
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Georg Keller
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Lorna Ulbrich
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Anja Fischer
Proceedings of the First Workshop on Scholarly Document Processing
This document demonstrates our groups approach to the CL-SciSumm shared task 2020. There are three tasks in CL-SciSumm 2020. In Task 1a, we apply a Siamese neural network to identify the spans of text in the reference paper best reflecting a citation. In Task 1b, we use a SVM to classify the facet of a citation.
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Co-authors
- Matthias Hagen 1
- Maik Fröbe 1
- Martin Potthast 1
- Maik Boltze 1
- Georg Keller 1
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