  To convince readers of the novelty of their research paper, authors must
perform a literature review and compose a coherent story that connects and
relates prior works to the current work. This challenging nature of literature
review writing makes automatic related work generation (RWG) academically and
computationally interesting, and also makes it an excellent test bed for
examining the capability of SOTA natural language processing (NLP) models.
Since the initial proposal of the RWG task, its popularity has waxed and waned,
following the capabilities of mainstream NLP approaches. In this work, we
survey the zoo of RWG historical works, summarizing the key approaches and task
definitions and discussing the ongoing challenges of RWG.
