  Generative Artificial Intelligence has grown exponentially as a result of
Large Language Models (LLMs). This has been possible because of the impressive
performance of deep learning methods created within the field of Natural
Language Processing (NLP) and its subfield Natural Language Generation (NLG),
which is the focus of this paper. Within the growing LLM family are the popular
GPT-4, Bard and more specifically, tools such as ChatGPT have become a
benchmark for other LLMs when solving most of the tasks involved in NLG
research. This scenario poses new questions about the next steps for NLG and
how the field can adapt and evolve to deal with new challenges in the era of
LLMs. To address this, the present paper conducts a review of a representative
sample of surveys recently published in NLG. By doing so, we aim to provide the
scientific community with a research roadmap to identify which NLG aspects are
still not suitably addressed by LLMs, as well as suggest future lines of
research that should be addressed going forward.
