@inproceedings{wozny-lango-2023-generating,
title = "Generating clickbait spoilers with an ensemble of large language models",
author = "Wo{\'z}ny, Mateusz and
Lango, Mateusz",
editor = "Keet, C. Maria and
Lee, Hung-Yi and
Zarrie{\ss}, Sina",
booktitle = "Proceedings of the 16th International Natural Language Generation Conference",
month = sep,
year = "2023",
address = "Prague, Czechia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.inlg-main.32",
doi = "10.18653/v1/2023.inlg-main.32",
pages = "431--436",
abstract = "Clickbait posts are a widespread problem in the webspace. The generation of spoilers, i.e. short texts that neutralize clickbait by providing information that makes it uninteresting, 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.",
}
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%0 Conference Proceedings
%T Generating clickbait spoilers with an ensemble of large language models
%A Woźny, Mateusz
%A Lango, Mateusz
%Y Keet, C. Maria
%Y Lee, Hung-Yi
%Y Zarrieß, Sina
%S Proceedings of the 16th International Natural Language Generation Conference
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F wozny-lango-2023-generating
%X Clickbait posts are a widespread problem in the webspace. The generation of spoilers, i.e. short texts that neutralize clickbait by providing information that makes it uninteresting, 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.
%R 10.18653/v1/2023.inlg-main.32
%U https://aclanthology.org/2023.inlg-main.32
%U https://doi.org/10.18653/v1/2023.inlg-main.32
%P 431-436
Markdown (Informal)
[Generating clickbait spoilers with an ensemble of large language models](https://aclanthology.org/2023.inlg-main.32) (Woźny & Lango, INLG-SIGDIAL 2023)
ACL