mlsanityData to 14 Oct 2026

Simple and Effective Baselines for Code Summarisation Evaluation

arXiv 2505.19392 ↗First posted 26 May 2025cs.CL cs.AI cs.SEcited by 0

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Abstract (arXiv)

Code documentation is useful, but writing it is time-consuming. Different techniques for generating code summaries have emerged, but comparing them is difficult because human evaluation is expensive and automatic metrics are unreliable. In this paper, we introduce a simple new baseline in which we ask an LLM to give an overall score to a summary. Unlike n-gram and embedding-based baselines, our approach is able to consider the code when giving a score. This allows us to also make a variant that does not consider the reference summary at all, which could be used for other tasks, e.g., to evaluate the quality of documentation in code bases. We find that our method is as good or better than prior metrics, though we recommend using it in conjunction with embedding-based methods to avoid the risk of LLM-specific bias.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 64 references that are in the map. First posted on arXiv 26 May 2025.

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31 of 64 references.

References · 31

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19 more
  1. 2020Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summarycited by 88
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  13. 2024Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translationcited by 16
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  19. 2024Programmer Visual Attention During Context-Aware Code Summarizationcited by 2

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