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.
@article{arxiv.1805.10395,
title = {Automatic Summarization of Student Course Feedback},
author = {Wencan Luo and Fei Liu and Zitao Liu and Diane Litman},
journal= {arXiv preprint arXiv:1805.10395},
year = {2018}
}