Artur Jurk


2022

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Clickbait Spoiling via Question Answering and Passage Retrieval
Matthias Hagen | Maik Fröbe | Artur Jurk | 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

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1A-Team / Martin-Luther-Universität Halle-Wittenberg@CLSciSumm 20
Artur Jurk | Maik Boltze | Georg Keller | Lorna Ulbrich | 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.