  Traditional evaluation metrics like ROUGE compare lexical overlap between the
reference and generated summaries without taking argumentative structure into
account, which is important for legal summaries. In this paper, we propose a
novel legal summarization evaluation framework that utilizes GPT-4 to generate
a set of question-answer pairs that cover main points and information in the
reference summary. GPT-4 is then used to generate answers based on the
generated summary for the questions from the reference summary. Finally, GPT-4
grades the answers from the reference summary and the generated summary. We
examined the correlation between GPT-4 grading with human grading. The results
suggest that this question-answering approach with GPT-4 can be a useful tool
for gauging the quality of the summary.
