To better capture the intent and purpose of human-written code summaries in the evaluation of LLM-generated summaries, researchers can consider incorporating more nuanced evaluation metrics that go beyond simple similarity measures. One approach could involve developing metrics that assess the relevance and context of the summary in relation to the code snippet. For example, metrics that evaluate the informativeness, clarity, and conciseness of the summary can provide a more comprehensive understanding of how well the LLM-generated summary aligns with the human-written summary. Additionally, incorporating user studies or feedback from developers can offer valuable insights into the perceived usefulness and effectiveness of the summaries in real-world scenarios. By integrating a combination of qualitative and quantitative evaluation methods that focus on capturing the holistic nature of human-written summaries, researchers can enhance the evaluation process and ensure that LLM-generated summaries align more closely with the intent and purpose of their human-written counterparts.