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Goyal, P., Kaushik, P., Gupta, P., Vashisth, D., Agarwal, S. and Goyal, N. (2020) Multilevel Event Detection, Storyline Generation, and Summarization for Tweet Streams. IEEE Transactions on Computational Social Systems, 7, 8-23.
https://doi.org/10.1109/tcss.2019.2954116

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.