Academic research is an exploration activity to solve problems that have never been resolved before. By this nature, each academic research work is required to perform a literature review to distinguish its novelties that have not been addressed by prior works. In natural language processing, this literature review is usually conducted under the "Related Work" section. The task of automatic related work generation aims to automatically generate the "Related Work" section given the rest of the research paper and a list of cited papers. Although this task was proposed over 10 years ago, it received little attention until very recently, when it was cast as a variant of the scientific multi-document summarization problem. However, even today, the problems of automatic related work and citation text generation are not yet standardized. In this survey, we conduct a meta-study to compare the existing literature on related work generation from the perspectives of problem formulation, dataset collection, methodological approach, performance evaluation, and future prospects to provide the reader insight into the progress of the state-of-the-art studies, as well as and how future studies can be conducted. We also survey relevant fields of study that we suggest future work to consider integrating.
翻译:学术研究是一项旨在解决以前从未解决的问题的探索活动。根据这种性质,每项学术研究工作都必须进行文献审查,以区分以前作品没有涉及的新颖之处。在自然语言处理中,这种文献审查通常是在“相关工作”一节下进行的。自动相关工作生成的任务旨在自动产生“相关工作”一节,因为研究论文的其余部分和列举的文件清单是自动产生的。尽管这项任务是10多年前提出的,但直到最近才得到多少关注,当时它被作为科学多文件汇总问题的变体推出。然而,即使是今天,与自动相关工作和引文生成有关的问题还没有标准化。在这次调查中,我们进行了元研究,从问题拟订、数据集收集、方法方法、业绩评估以及未来前景的角度,将有关工作生成的现有文献进行比较,以便让读者深入了解最新研究的进展,以及今后如何进行研究。我们还调查了相关的研究领域,我们建议今后的工作考虑整合。