  Natural Language Generation systems typically have two parts - strategic
('what to say') and tactical ('how to say'). We present our experiments in
building an unsupervised corpus-driven template based tactical NLG system. We
consider templates as a sequence of words containing gaps. Our idea is based on
the observation that templates are grammatical locally (within their textual
span). We posit the construction of a sentence as a highly restricted sequence
of such templates. This work is an attempt to explore the resulting search
space using Genetic Algorithms to arrive at acceptable solutions. We present a
baseline implementation of this approach which outputs gapped text.
