 Dataset & preprocessing The model was finetuned on a corpus of news articles from [BR24](https://www.br.de/) published between  2015 and 2021. The texts are in german language and cover a range of different news topics like politics, sports, and culture, with a focus on topics that are relevant to the people living in Bavaria (Germany).  In a preprocessing step, article-headline pairs matching any of the following criteria were filtered out: - very short articles (number of words in text lower than 3x the number of words in the headline). - articles with headlines containing only words that are not contained in the text (lemmatized and excluding stopwords). - articles with headlines that are just the name of a known text format (e.g. "Das war der Tag" a format summarizing the most important topics of the day)  Further the prefix `summarize: ` was added to all articles to make use of the pretrained summarization capabilities of mT5.  After filtering the corpus contained 89098 article-headline pairs, of which 87306 were used for training, 902 for validation, and 890 for testing.  