通过将文章分解为事实与观点并检索实体实现面向问题的摘要生成
Question-focused Summarization by Decomposing Articles into Facts and Opinions and Retrieving Entities
AI总结：
本研究针对股价波动预测与市场变革早期识别需求，提出将新闻分解为事实事件、结合实体生成元组摘要再整合为全文摘要的方法，借助GPT 3.5与多源数据构建企业实体关联，为金融从业者提供辅助决策的趋势早发现系统。
摘要：
本研究聚焦于利用自然语言处理技术预测股价波动，尤其关注对经济、政治、社会及技术变革的早期识别，这类变革可被用于捕捉市场机遇。所提出的方法包括：首先从新闻文章中识别出显著的事实与事件，随后利用这些事实与实体构建元组，这些元组可用于生成特定实体对应的市场变化摘要，最后整合所有摘要以形成整篇文章的最终抽象摘要。本研究旨在通过分析Wikipedia数据与《经济学人》的文章，建立企业与实体之间的关联。研究采用大语言模型GPT 3.5生成各类摘要并构建最终摘要。本研究的终极目标是开发一套综合系统，通过实现对市场趋势与事件的早期识别，为金融分析师和投资者提供更具信息支撑的决策工具。
英文摘要：
This research focuses on utilizing natural language processing techniques to predict stock price fluctuations, with a specific interest in early detection of economic, political, social, and technological changes that can be leveraged for capturing market opportunities. The proposed approach includes the identification of salient facts and events from news articles, then use these facts to form tuples with entities which can be used to get summaries of market changes for particular entity and then finally combining all the summaries to form a final abstract summary of the whole article. The research aims to establish relationships between companies and entities through the analysis of Wikipedia data and articles from the Economist. Large Language Model GPT 3.5 is used for getting the summaries and also forming the final summary. The ultimate goal of this research is to develop a comprehensive system that can provide financial analysts and investors with more informed decision-making tools by enabling early detection of market trends and events.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
