Automatic abstractive text summarization is a challenging task in the field of natural language processing. This paper presents a model for domain-specific sum marization for Danish news articles, Dan SumT5; an mT5 model fine-tuned on a cleaned subset of the DaNewsroom dataset consisting of abstractive summary-article pairs. The resulting state-of-the-art model is evaluated both quantitatively and qualitatively, using ROUGE and BERTScore metrics and human rankings of the summaries. We find that although model refinements increase quantitative and qualitative performance, the model is still prone to factual errors. We discuss the limitations of current evaluation methods for automatic abstractive summarization and underline the need for improved metrics and transparency within the field. We suggest that future work should employ methods for detecting and reducing errors in model output and methods for referenceless evaluation of summaries.