From a commercial perspective, I think NLG is currently most successful in financial reporting. Although of course there are many great NLG applications in other sectors!
Some thoughts on how to vary words in NLG text. This is aimed at practioners who are building NLG systems, not researchers.
An approach I often take to NLG and indeed AI is to try to understand underying linguistic, NLG, and AI issues, and then to look for simple solutions to these issues.
Some thoughts on language grounding, especially choosing words to express data, and how this depends on context.
Unfortunately I suspect many researchers make their results looks better by using poor baselines. I give some thoughts on this, based on a recent discussion with a PhD student.
Some thoughts about when I feel comfortable being a coauthor on a paper, expressed as a letter to someone who put me on a paper as a co-author without asking me frst,
Some musings on principled and theoretically sound techniques for automatically evaluating NLG systems.