Deriving content selection rules from a corpus of non-naturally occurring documents for a novel NLG application

We describe a methodology for deriving content selection rules for NLG applications that aim to replace oral communications from human experts by written communications that are generated automatically. We argue for greater involvement of users and for a strategy for handling sparse data