A Multi-grained Dataset for News Event Triggered Knowledge Update

Yu-Ting Lee, Ying-Jhe Tang, Yu-Chung Cheng, Pai-Lin Chen, Tsai-Yen Li, Hen-Hsen Huang

Abstract: 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.