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Lu, X.S., Zhou, M., Qi, L. and Liu, H. (2019) Clustering-Algorithm-Based Rare-Event Evolution Analysis via Social Media Data. IEEE Transactions on Computational Social Systems, 6, 301-310.
https://doi.org/10.1109/tcss.2019.2898774

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.