政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152565

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152565

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/152565

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/152565

Title:	適應性新聞生成：整合大型語言模型和圖神經網路以模仿新聞媒體立場風格 Adaptive News Generation: Integrating large language models and graph neural networks to mimic newspaper stances
Authors:	彭怡靜 Peng, Yi-Ching
Contributors:	李蔡彥黃瀚萱 Li, Tsai-Yen Huang, Hen-Hsen 彭怡靜 Peng, Yi-Ching
Keywords:	大型語言模型圖神經網路自動新聞學文本生成自然語言生成 Large Language Model Graph Neural Network Automated Journalism Text Generation Natural Language Generation
Date:	2024
Issue Date:	2024-08-05 12:44:28 (UTC+8)
Abstract:	每日國內、外皆有大量事件發生，新聞媒體需要在一天內快速發布新聞以傳遞最新的資訊，大量新聞發布的需求使得新聞媒體需要耗費大量成本產生報導。隨著自然語言處理（Natural Language Processing, NLP）的技術快速發展，相關的應用也廣泛應用在各個領域，像是美聯社、華盛頓郵報等國外報社已應用自動新聞學（Automated Journalism）來幫助新聞的生成，但此類自動新聞學的方法需要依靠結構化的表格，例如財報、得分紀錄等，才可以幫助新聞生成。本研究希望可以降低新聞媒體產出新聞的成本，將媒體發布的新聞內容及其他屬性建構成圖，並以圖神經網路（Graph Neural Network, GNN）的概念透過大型語言模型（Large Language Model, LLM）的幫助來生成新聞，生成符合事實且文句通暢的新聞。本研究證實透過我們的方法可以生成人、事、地及寫作風格都接近目標報社的新聞，並且兼顧了初步新聞生成的速度及品質；我們也設計了Systemized Auto Prompt Engineering的新方法，以自動化調整包含多個文字提示的系統。 Daily, numerous events occur both domestically and internationally, requiring media outlets to quickly disseminate news. This demand imposes significant costs on news organizations. With the rapid advancement of Natural Language Processing (NLP) technology, its applications have expanded widely. Newspapers like the Associated Press and The Washington Post have implemented Automated Journalism to facilitate news generation. However, current methods in automated journalism rely on structured data tables, such as financial reports and score records. This study aims to mitigate the cost associated with news production by structuring media-released news content and other attributes into a graph. Leveraging the concepts of Graph Neural Networks (GNNs) and the assistance of Large Language Models (LLMs), we seek to generate news efficiently without relying on structured tables, thereby contributing to a more cost-effective news production process. This study confirms that our method can generate news articles closely matching the target media in terms of people, events, locations, and style, balancing speed and quality. Additionally, we designed Systemized Auto Prompt Engineering to automate system adjustments with multiple text prompts.
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Description:	碩士國立政治大學資訊科學系 111753106
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0111753106
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

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