  Recent years have seen a number of proposals for performing Natural Language
Generation (NLG) based in large part on statistical techniques. Despite having
many attractive features, we argue that these existing approaches nonetheless
have some important drawbacks, sometimes because the approach in question is
not fully statistical (i.e., relies on a certain amount of handcrafting),
sometimes because the approach in question lacks transparency. Focussing on
some of the key NLG tasks (namely Content Selection, Lexical Choice, and
Linguistic Realisation), we propose a novel approach, called the Text
Reassembling approach to NLG (TRG), which approaches the ideal of a purely
statistical approach very closely, and which is at the same time highly
transparent. We evaluate the TRG approach and discuss how TRG may be extended
to deal with other NLG tasks, such as Document Structuring, and Aggregation. We
discuss the strengths and limitations of TRG, concluding that the method may
hold particular promise for domain experts who want to build an NLG system
despite having little expertise in linguistics and NLG.
