通过大型语言模型进行多文档摘要的事件链提示

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Web Information Systems Pub Date : 2024-02-15 DOI:10.1108/ijwis-12-2023-0249
Songlin Bao, Tiantian Li, Bin Cao
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引用次数: 0

摘要

目的 在大数据时代,各行各业每天都会产生大量文本数据。对这些数据进行简化和总结,可以有效地服务用户,提高效率。最近,大型语言模型(LLM)中的零点提示在各种语言任务中表现出了卓越的性能。然而,生成非常 "简洁 "的多文档摘要对它来说是一项艰巨的任务。当零次提示中指定了简洁性时,生成的多文档摘要仍然包含一些不重要的信息,即使是少量提示也是如此。为了克服这一难题,作者提出了针对多文档摘要(MDS)任务的事件链(CoE)提示法。在这个提示过程中,作者以事件为中心,提出了一个四步总结推理过程:特定事件提取;事件抽象和概括;常见事件统计;总结生成。为了进一步提高 LLM 的性能,作者以摘要推理为例对 CoE 提示进行了扩展。作者在两个数据集上对其提出的提示方法进行了评估。在 ChatGLM2-6b 上的实验结果表明,在所有数据集上,作者提出的 CoE 提示始终优于其他典型提示。CoE 提示不仅能识别关键事件,还能确保摘要的简洁性。通过这种方法,用户可以快速获取最相关、最重要的信息,从而改善决策过程。
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Chain-of-event prompting for multi-document summarization by large language models
Purpose In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task. Design/methodology/approach To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning. Findings Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets. Originality/value This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.
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来源期刊
International Journal of Web Information Systems
International Journal of Web Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
4.60
自引率
0.00%
发文量
19
期刊介绍: The Global Information Infrastructure is a daily reality. In spite of the many applications in all domains of our societies: e-business, e-commerce, e-learning, e-science, and e-government, for instance, and in spite of the tremendous advances by engineers and scientists, the seamless development of Web information systems and services remains a major challenge. The journal examines how current shared vision for the future is one of semantically-rich information and service oriented architecture for global information systems. This vision is at the convergence of progress in technologies such as XML, Web services, RDF, OWL, of multimedia, multimodal, and multilingual information retrieval, and of distributed, mobile and ubiquitous computing. Topicality While the International Journal of Web Information Systems covers a broad range of topics, the journal welcomes papers that provide a perspective on all aspects of Web information systems: Web semantics and Web dynamics, Web mining and searching, Web databases and Web data integration, Web-based commerce and e-business, Web collaboration and distributed computing, Internet computing and networks, performance of Web applications, and Web multimedia services and Web-based education.
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