Zhang, H., Li, G.H., Sun, B.L. and Jia, L. (2013) Modeling News Event Evolution. Journal of National University of Defense Technology, 35, 166-170.
http://journal.nudt.edu.cn/gfkjdxxb/ch/reader/view_abstract.aspx?file_no=201304029&flag=1

has been cited by the following article:

• TITLE: Storyline Extraction of Document-Level Events Using Large Language Models

  AUTHORS: Ziyang Hu, Yaxiong Li

  KEYWORDS: Document-Level Storyline Extraction, Timeline, Large Language Models, Topological Structure of Storyline, Prompt Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.11, November 27, 2024

  ABSTRACT: This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prompt + one-shot learning proposed in this article works well. Meanwhile, our research findings indicate that although timeline-based storyline extraction has shown promising prospects in the practical applications of LLMs, it is still a complex natural language processing task that requires further research.