  We suggest a new NLG task in the context of the discourse generation pipeline
of computational storytelling systems. This task, textual embellishment, is
defined by taking a text as input and generating a semantically equivalent
output with increased lexical and syntactic complexity. Ideally, this would
allow the authors of computational storytellers to implement just lightweight
NLG systems and use a domain-independent embellishment module to translate its
output into more literary text. We present promising first results on this task
using LSTM Encoder-Decoder networks trained on the WikiLarge dataset.
Furthermore, we introduce "Compiled Computer Tales", a corpus of
computationally generated stories, that can be used to test the capabilities of
embellishment algorithms.
