# Automatic Summarization of Student Course Feedback

arXiv: https://arxiv.org/abs/1805.10395 · HTML: https://mlsanity.com/paper/1805.10395/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Improving Online Forums Summarization via Hierarchical Unified Deep Neural Network](https://mlsanity.com/paper/2103.13587/) (2021)
- [ReflectSumm: A Benchmark for Course Reflection Summarization](https://mlsanity.com/paper/2403.19012/) (2024)
- [Arabic Dialect Classification using RNNs, Transformers, and Large Language Models: A Comparative Analysis](https://mlsanity.com/paper/2506.19753/) (2025)

## Abstract (arXiv)

Student course feedback is generated daily in both classrooms and online course discussion forums. Traditionally, instructors manually analyze these responses in a costly manner. In this work, we propose a new approach to summarizing student course feedback based on the integer linear programming (ILP) framework. Our approach allows different student responses to share co-occurrence statistics and alleviates sparsity issues. Experimental results on a student feedback corpus show that our approach outperforms a range of baselines in terms of both ROUGE scores and human evaluation.