政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/148475

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/148475

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题名:	新聞事件觸發之全文知識更新 Knowledge Update in Full Text Triggered by a News Event
作者:	李昱廷 Lee, Yu-Ting
贡献者:	李蔡彥黃瀚萱 Li, Tsai-Yen Huang, Hen-Hsen 李昱廷 Lee, Yu-Ting
关键词:	文本生成時序知識建模知識更新自然語言生成大型語言模型內容改寫新聞事件 Text Generation Temporal Knowledge Modeling Update Summarization Natural Language Generation Knowledge Update Large Language Model Text Revision News Event
日期:	2023
上传时间:	2023-12-01 10:34:04 (UTC+8)
摘要:	在網路資訊的快速發展下，每日的新聞事件更迭與知識獲取已成為人們主要獲取資訊的管道，新的知識內容每分每秒都不斷地在發生，而保持資訊的更新也需要極大的人力和時間成本。在本研究中，我們規劃了一項新的自然語言生成任務，即新聞事件觸發之知識更新。研究目標以現有關於某主題的文章或舊版本的內容和一個關於該主題的新聞事件，根據該新聞事件的資訊生成一篇更新後的文章。在資料蒐集的過程，我們建立一個多粒度的新聞資料集以適用於研究目標。蒐集主要源自於維基百科的文章，經由爬取並與多種語言的新聞事件對齊，而資料集包含有引文、文章之首段和文章的全文。我們提出改良後的模型設計用於自動化更新全文知識，並以多個大型語言模型驗證模型架構之有效性。 With the rapid development of internet information, daily news events and knowledge acquisition have become the primary channels for people to access information. New knowledge content is constantly emerging every second, requiring significant human and time resources to ensure knowledge updates. In this research, we propose a new natural language generation task, namely ”Knowledge Update in Full Text Triggered by a News Event”. Our objective is to generate an updated article based on a given news event and existing articles or old versions of content on a specific topic. To facilitate our research objective, we construct a multi-granularity news dataset suitable for our task. The dataset is primarily sourced from Wikipedia articles, crawled and aligned with news events in multiple language units. Dataset includes citations, first paragraphs, and full-text articles. We present an improved model architecture tailored specifically for the task of updating knowledge in full-content articles and validate the effectiveness of our framework with multiple large language models.
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描述:	碩士國立政治大學資訊科學系 110753204
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0110753204
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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