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Wang, H. and Koh, J.L. (2017) Timeline Summarization for Event-Related Discussions on a Chinese Social Media Platform. In: Advances in Artificial Intelligence: From Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, Springer, 579-594.
https://link.springer.com/chapter/10.1007/978-3-319-60042-0_64

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.