@inproceedings{francis-etal-2024-gattina,
title = "{GATTINA} - {G}ener{A}tion of {T}i{T}les for {I}talian News Articles: A {CALAMITA} Challenge",
author = "Francis, Maria and
Rinaldi, Matteo and
Gili, Jacopo and
De Cosmo, Leonardo and
Iannaccone, Sandro and
Nissim, Malvina and
Patti, Viviana",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.121/",
pages = "1094--1105",
ISBN = "979-12-210-7060-6",
abstract = "We introduce a new benchmark designed to evaluate the ability of Large Language Models (LLMs) to generate Italian-language headlines for science news articles. The benchmark is based on a large dataset of science news articles obtained from Ansa Scienza and Galileo, two important Italian media outlets. Effective headline generation requires more than summarizing article content; headlines must also be informative, engaging, and suitable for the topic and target audience, making automatic evaluation particularly challenging. To address this, we propose two novel transformer-based metrics to assess headline quality. We aim for this benchmark to support the evaluation of Italian LLMs and to foster the development of tools to assist in editorial workflows."
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%0 Conference Proceedings
%T GATTINA - GenerAtion of TiTles for Italian News Articles: A CALAMITA Challenge
%A Francis, Maria
%A Rinaldi, Matteo
%A Gili, Jacopo
%A De Cosmo, Leonardo
%A Iannaccone, Sandro
%A Nissim, Malvina
%A Patti, Viviana
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F francis-etal-2024-gattina
%X We introduce a new benchmark designed to evaluate the ability of Large Language Models (LLMs) to generate Italian-language headlines for science news articles. The benchmark is based on a large dataset of science news articles obtained from Ansa Scienza and Galileo, two important Italian media outlets. Effective headline generation requires more than summarizing article content; headlines must also be informative, engaging, and suitable for the topic and target audience, making automatic evaluation particularly challenging. To address this, we propose two novel transformer-based metrics to assess headline quality. We aim for this benchmark to support the evaluation of Italian LLMs and to foster the development of tools to assist in editorial workflows.
%U https://aclanthology.org/2024.clicit-1.121/
%P 1094-1105
Markdown (Informal)
[GATTINA - GenerAtion of TiTles for Italian News Articles: A CALAMITA Challenge](https://aclanthology.org/2024.clicit-1.121/) (Francis et al., CLiC-it 2024)
ACL