# Comparative Study and Framework for Automated Summariser Evaluation: LangChain and Hybrid Algorithms

arXiv: https://arxiv.org/abs/2310.02759 · HTML: https://mlsanity.com/paper/2310.02759/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [Automated Essay Scoring Using Transformer Models](https://mlsanity.com/paper/2110.06874/) (2021)
- [Automated Evaluation for Student Argumentative Writing: A Survey](https://mlsanity.com/paper/2205.04083/) (2022)
- [Automated Essay Scoring in Argumentative Writing: DeBERTeachingAssistant](https://mlsanity.com/paper/2307.04276/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Multi-Agent Framework Integrating Large Language Models and Generative AI for Accelerated Metamaterial Design](https://mlsanity.com/paper/2503.19889/) (2025)

## Abstract (arXiv)

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.