  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.
