Perhaps the most common reason for bad NLG output texts is low-quality input data. Ie, Garbage In, Garbage Out is true regardless of our technology.
I am now chair of ACL SIGGEN. I hope SIGGEN can help the NLG community by encouraging high-quality scientific research, strengthening interaction with the non-NLP world, and providing trusted unbiased information about NLG.
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.
Some musings on principled and theoretically sound techniques for automatically evaluating NLG systems.
BLEU works much better for MT systems and NLG systems. In this blog I present some speculations as to why this is the case.
Some comments on how different components in the NLG pipeline can “add value” by contributing to the ultimate goal of generating texts that easy for people to read and understand.