Storyline Extraction of Document-Level Events Using Large Language Models  

作　　者：Ziyang Hu Yaxiong Li Ziyang Hu;Yaxiong Li(School of Foreign Studies, Zhongnan University of Economics and Law, Wuhan, China;Information Center, Hubei University of Science and Technology, Wuhan, China)

机构地区：[1]School of Foreign Studies, Zhongnan University of Economics and Law, Wuhan, China [2]Information Center, Hubei University of Science and Technology, Wuhan, China

出　　处：《Journal of Computer and Communications》2024年第11期162-172,共11页电脑和通信（英文）

摘　　要：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.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.

关 键 词：Document-Level Storyline Extraction TIMELINE Large Language Models Topological Structure of Storyline Prompt Learning 

分 类 号：H31[语言文字—英语]