Automated text summarization is particularly important in the legal domain due to the length and inherent complexity of the analyzed documents. The Legal AI community has already started to address the text summarization problem. However, most existing approaches focus on English-written documents. Up to now, limited efforts have been devoted to summarizing Italian legal documents. Existing approaches extract portions of existing content without rephrasing them. To bridge this gap, in this work we aim at generating abstractive summaries of Italian legal news. We propose to condense the original news content into different summary types, i.e., an abstract, a title, or a subheader. We benchmark different state-of-the-art summarization models to generate abstractive summaries of Italian legal news. We also investigate the suitability of augmented models capable of handling long Italian documents. The experimental results achieved on a proprietary Italian dataset show the effectiveness of abstractive models in generating fairly accurate summaries and the importance of using larger contextual windows to generate news abstracts.