  Clickbait posts are a widespread problem in the webspace. The generation of
spoilers, i.e. short texts that neutralize clickbait by providing information
that satisfies the curiosity induced by it, is one of the proposed solutions to
the problem. Current state-of-the-art methods are based on passage retrieval or
question answering approaches and are limited to generating spoilers only in
the form of a phrase or a passage. In this work, we propose an ensemble of
fine-tuned large language models for clickbait spoiler generation. Our approach
is not limited to phrase or passage spoilers, but is also able to generate
multipart spoilers that refer to several non-consecutive parts of text.
Experimental evaluation demonstrates that the proposed ensemble model
outperforms the baselines in terms of BLEU, METEOR and BERTScore metrics.
