  Automated headline generation for online news articles is not a trivial task
- machine generated titles need to be grammatically correct, informative,
capture attention and generate search traffic without being "click baits" or
"fake news". In this paper we showcase how a pre-trained language model can be
leveraged to create an abstractive news headline generator for German language.
We incorporate state of the art fine-tuning techniques for abstractive text
summarization, i.e. we use different optimizers for the encoder and decoder
where the former is pre-trained and the latter is trained from scratch. We
modify the headline generation to incorporate frequently sought keywords
relevant for search engine optimization. We conduct experiments on a German
news data set and achieve a ROUGE-L-gram F-score of 40.02. Furthermore, we
address the limitations of ROUGE for measuring the quality of text
summarization by introducing a sentence similarity metric and human evaluation.
