Related Work

Automatic related work generation (RWG) aims to automate the creation of literature review sections in scientific papers, a crucial but time-consuming task for researchers. Current research focuses on developing sophisticated models, often leveraging transformer architectures and contrastive learning, to generate comprehensive related work sections from full-text papers rather than just abstracts, improving the quality and comprehensiveness of the generated text. This field is significant because efficient RWG could substantially accelerate the research process, enabling scientists to more effectively synthesize existing knowledge and focus on novel contributions.