  In the era of Big Data and Deep Learning, there is a common view that machine
learning approaches are the only way to cope with the robust and scalable
information extraction and summarization. It has been recently proposed that
the CNL approach could be scaled up, building on the concept of embedded CNL
and, thus, allowing for CNL-based information extraction from e.g. normative or
medical texts that are rather controlled by nature but still infringe the
boundaries of CNL. Although it is arguable if CNL can be exploited to approach
the robust wide-coverage semantic parsing for use cases like media monitoring,
its potential becomes much more obvious in the opposite direction: generation
of story highlights from the summarized AMR graphs, which is in the focus of
this position paper.
