Comparative Study and Framework for Automated Summariser Evaluation: LangChain and Hybrid Algorithms
This work addresses the need for reliable automated summarizer evaluation in educational contexts, though it appears incremental as it builds on existing scoring techniques and tools.
The research tackled the problem of evaluating automated summarizers by developing a framework using LangChain and hybrid algorithms to gauge user understanding of summarized PDF content, with results aimed at enhancing learning analytics and ability.
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.