Related Work and Citation Text Generation: A Survey
It provides a comprehensive overview for researchers in NLP, but it is incremental as it synthesizes existing work without introducing new methods.
The paper surveys the field of automatic related work generation (RWG), summarizing key approaches and task definitions while discussing ongoing challenges in this area.
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.