 Limitations Like most state-of-the-art summarization models this model has issues with the factuality of the generated texts [^factuality]. **It is therefore strongly advised having a human fact-check the generated headlines.**  An analysis of possible biases reproduced by the present model, regardless of whether they originate from our finetuning or the underlying mT5 model, is beyond the scope of this work. We assume that biases exist within the model and an analysis will be a task for future work  As the model was trained on news articles from the time range 2015-2021, further biases and factual errors could emerge due to topic shifts in news articles and changes in the (e.g. political) situation.  