An overview of sentence ordering task

Abstract

AbstractThe sentence ordering task aims to organize complex, unordered sentences into readable text. This improves accuracy, validity, and reliability in various natural language processing domains, including automatic text generation, text summarization, and machine translation. We begin by analyzing and summarizing the sentence ordering algorithm from two perspectives: the input data approach and the implementation technique approach. Based on the different ways of input data formats, they are classified into pointwise, pairwise, and listwise, and the advantages, disadvantages and representative algorithmic features of each are discussed. Based on the different implementation technologies, we classify them into sentence ordering algorithms based on learning to rank and deep learning, and the core ideas, typical algorithms and research progress of these two categories of methods were specifically explained. We summarize the datasets and evaluation metrics of currently commonly used sentence ordering tasks. Additionally, we analyze the problems and challenges of sentence ordering tasks and look forward to the future direction of this field.