  Text generation has become more accessible than ever, and the increasing
interest in these systems, especially those using large language models, has
spurred an increasing number of related publications. We provide a systematic
literature review comprising 244 selected papers between 2017 and 2024. This
review categorizes works in text generation into five main tasks: open-ended
text generation, summarization, translation, paraphrasing, and question
answering. For each task, we review their relevant characteristics, sub-tasks,
and specific challenges (e.g., missing datasets for multi-document
summarization, coherence in story generation, and complex reasoning for
question answering). Additionally, we assess current approaches for evaluating
text generation systems and ascertain problems with current metrics. Our
investigation shows nine prominent challenges common to all tasks and sub-tasks
in recent text generation publications: bias, reasoning, hallucinations,
misuse, privacy, interpretability, transparency, datasets, and computing. We
provide a detailed analysis of these challenges, their potential solutions, and
which gaps still require further engagement from the community. This systematic
literature review targets two main audiences: early career researchers in
natural language processing looking for an overview of the field and promising
research directions, as well as experienced researchers seeking a detailed view
of tasks, evaluation methodologies, open challenges, and recent mitigation
strategies.
