Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Clickbait Spoiling via Question Answering and Passage Retrieval   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.

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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: stat
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The categories of this paper are: econ
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The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an