自动化摘要器评估的比较研究与框架：LangChain与混合算法
Comparative Study and Framework for Automated Summariser Evaluation: LangChain and Hybrid Algorithms
AI总结：
本文提出基于LangChain与混合算法的自动化摘要评估框架，利用LLM评分指标衡量用户对PDF摘要内容的理解，以支持学习分析。
摘要：
自动作文评分（AES）已被证明是前沿技术之一。评分技术被用于多种目的。可靠的分数是基于有影响力的变量计算得出的。这些变量可以根据领域通过不同方法进行计算。本研究聚焦于用户对给定主题的理解。分析基于使用大型语言模型的评分指标。用户随后可以比较和对照他们最近学习的某个主题的理解情况。这些结果随后用于学习分析，并在提升学习能力方面取得进展。在本研究中，重点是对PDF文档进行摘要，并衡量用户对其内容的理解。该过程涉及利用Langchain工具对PDF进行摘要并提取基本信息。通过采用这种技术，研究旨在确定用户对摘要内容的理解程度。
英文摘要：
Automated Essay Score (AES) is proven to be one of the cutting-edge technologies. Scoring techniques are used for various purposes. Reliable scores are calculated based on influential variables. Such variables can be computed by different methods based on the domain. The research is concentrated on the user's understanding of a given topic. The analysis is based on a scoring index by using Large Language Models. The user can then compare and contrast the understanding of a topic that they recently learned. The results are then contributed towards learning analytics and progression is made for enhancing the learning ability. In this research, the focus is on summarizing a PDF document and gauging a user's understanding of its content. The process involves utilizing a Langchain tool to summarize the PDF and extract the essential information. By employing this technique, the research aims to determine how well the user comprehends the summarized content.
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。
