Generating clickbait spoilers with an ensemble of large language models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10475934" target="_blank" >RIV/00216208:11320/23:10475934 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2023.inlg-main.32/" target="_blank" >https://aclanthology.org/2023.inlg-main.32/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Generating clickbait spoilers with an ensemble of large language models
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 16th International Natural Language Generation Conference
ISBN
979-8-89176-001-1
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
431-436
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburg, PA, USA
Event location
Praha, Czechia
Event date
Sep 13, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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