Résultats du processus de synthèse de l'article arXiv : 2212.09577v1

Résumé : The publication rates are skyrocketing across many fields of science, and it is difficult to stay up to date with the latest research. This makes automatically summarizing the latest findings and helping scholars to synthesize related work in a given area an attractive research objective. In this paper we study the problem of citation text generation, where given a set of cited papers and citing context the model should generate a citation text. While citation text generation has been tackled in prior work, existing studies use different datasets and task definitions, which makes it hard to study citation text generation systematically. To address this, we propose CiteBench: a benchmark for citation text generation that unifies the previous datasets and enables standardized evaluation of citation text generation models across task settings and domains. Using the new benchmark, we investigate the performance of multiple strong baselines, test their transferability between the datasets, and deliver new insights into task definition and evaluation to guide the future research in citation text generation. We make CiteBench publicly available at https://github.com/UKPLab/citebench.