Summarizing Student Responses to Reflection Prompts

We propose to automatically summarize student responses to reflection prompts and introduce a novel summarization algo-rithm that differs from traditional methods in several ways. First, since the linguis-tic units of student inputs range from sin-gle words to multiple sentences, our sum-maries are created from extracted phrases rather than from sentences. Second, the phrase summarization algorithm ranks the phrases by the number of students who semantically mention a phrase in a sum-mary. Experimental results show that the proposed phrase summarization ap-proach achieves significantly better sum-marization performance on an engineering course corpus in terms of ROUGE scores when compared to other summarization methods, including MEAD, LexRank and MMR. 1