  Previous work of ours on Semantic Storytelling uses text analytics procedures
including Named Entity Recognition and Event Detection. In this paper, we
outline our longer-term vision on Semantic Storytelling and describe the
current conceptual and technical approach. In the project that drives our
research we develop AI-based technologies that are verified by partners from
industry. One long-term goal is the development of an approach for Semantic
Storytelling that has broad coverage and that is, furthermore, robust. We
provide first results on experiments that involve discourse parsing, applied to
a concrete use case, "Explore the Neighbourhood!", which is based on a
semi-automatically collected data set with documents about noteworthy people in
one of Berlin's districts. Though automatically obtaining annotations for
coherence relations from plain text is a non-trivial challenge, our preliminary
results are promising. We envision our approach to be combined with additional
features (NER, coreference resolution, knowledge graphs
