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    題名: A Multi-grained Dataset for News Event Triggered Knowledge Update
    作者: 鄭宇君
    Cheng, Yu chung
    Lee, Yu Ting;Tang, Ying Jhe;Chen, Pailin;Li, Tsai Yen;Huang, Hen Hsen
    貢獻者: 傳播學院
    日期: 2022-10
    上傳時間: 2023-06-16 16:42:08 (UTC+8)
    摘要: Keeping knowledge facts up-to-date is labored and costly as the world rapidly changes and new information emerges every second. In this work, we introduce a novel task, news event triggered knowledge update. Given an existing article about a topic with a news event about the topic, the aim of our task is to generate an updated article according to the information from the news event. We create a multi-grained dataset for the investigation of our task. The articles from Wikipedia are collected and aligned with news events at multiple language units, including the citation text, the first paragraph, and the full content of the news article. Baseline models are also explored at three levels of knowledge update, including the first paragraph, the summary, and the full content of the knowledge facts.
    關聯: CIKM `22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, ACM, pp.4158-4162
    資料類型: conference
    DOI 連結: https://doi.org/10.1145/3511808.3557537
    DOI: 10.1145/3511808.3557537
    顯示於類別:[傳播學院] 會議論文

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