The contribution presents REVERINO, a dataset composed of pairs of regesta and full texts of medieval papal documents, designed to support the automatic generation of regesta through artificial intelligence techniques. After describing the process of building the corpus from printed collections through a pipeline of annotation, extraction, and post-processing, the study uses the dataset as a benchmark to evaluate the ability of language models to summarize Latin texts. The results show significant limitations in the automatic generation of regesta, while highlighting performance differences among models and suggesting possible strategies for improvement.

Sabbatini, I. (2025). REVERINO: REgesta generation VERsus latIN summarizatiOn. In Proceedings of the 21st Conference on Information and Research science Connecting to Digital and Library science.

REVERINO: REgesta generation VERsus latIN summarizatiOn

Ilaria sabbatini
2025-01-01

Abstract

The contribution presents REVERINO, a dataset composed of pairs of regesta and full texts of medieval papal documents, designed to support the automatic generation of regesta through artificial intelligence techniques. After describing the process of building the corpus from printed collections through a pipeline of annotation, extraction, and post-processing, the study uses the dataset as a benchmark to evaluate the ability of language models to summarize Latin texts. The results show significant limitations in the automatic generation of regesta, while highlighting performance differences among models and suggesting possible strategies for improvement.
2025
Settore HIST-04/D - Paleografia
Sabbatini, I. (2025). REVERINO: REgesta generation VERsus latIN summarizatiOn. In Proceedings of the 21st Conference on Information and Research science Connecting to Digital and Library science.
File in questo prodotto:
File Dimensione Formato  
REVERINO REgesta generation VERsus latIN summarizatiOn.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Versione Editoriale
Dimensione 1.61 MB
Formato Adobe PDF
1.61 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/703968
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact