AUTHORS: Giovanni Puccetti Laura Righi Ilaria Sabbatini Andrea Esuli

WORK PACKAGE: WP 7 – REVER

URL: REVERINO: REgesta generation VERsus latINsummarizatiOn

Keywords: Regesta, Latin Text Summarization, Large Language Models, Digital Humanities

Abstract
In this work we introduce the REVERINO dataset, a collection of 4533 pairs of Latin regesta with their respective full text medieval pontifical document extracted from two collections, Epistolae saeculi XIII e regestis pontificum Romanorum selectae. (1216-1268) and Les Registres de Gregoire IX (1227/41). We describe the pipeline used to extract the text from the images of the printed pages and we make high level analysis of the corpus.
After developing REVERINO we use it as a benchmark to test the ability of Large Language Models (LLMs) to
generate the regestum of a given Latin text. We test 3 LLMs among the best performing ones, GPT-4o, Llama 3.1 70b and Llama 3.1 405b and find that GPT-4o is the best at generating text in Latin. Interestingly, we also find that for Llama models it can be beneficial to first generate a text in English and then translate it in Latin to write better regesta