# Context-aware Code Summary Generation

arXiv: https://arxiv.org/abs/2408.09006 · HTML: https://mlsanity.com/paper/2408.09006/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2024.

## Built on by (most cited first, 3 of 3)

- [BatCoder: Self-Supervised Bidirectional Code-Documentation Learning via Back-Translation](https://mlsanity.com/paper/2602.02554/) (2026)
- [The Illusion of Agentic Complexity in README.md Generation: Evaluating Single-Agent vs. Multi-Agent RAG Systems](https://mlsanity.com/paper/2606.30524/) (2026)
- [CoSTAR: Data Synthesis-Driven Constraint-Aware COBOL Section Summarization for Legacy System Modernization](https://mlsanity.com/paper/2609.11332/) (2026)

## Abstract (arXiv)

Code summary generation is the task of writing natural language descriptions of a section of source code. Recent advances in Large Language Models (LLMs) and other AI-based technologies have helped make automatic code summarization a reality. However, the summaries these approaches write tend to focus on a narrow area of code. The results are summaries that explain what that function does internally, but lack a description of why the function exists or its purpose in the broader context of the program. In this paper, we present an approach for including this context in recent LLM-based code summarization. The input to our approach is a Java method and that project in which that method exists. The output is a succinct English description of why the method exists in the project. The core of our approach is a 350m parameter language model we train, which can be run locally to ensure privacy. We train the model in two steps. First we distill knowledge about code summarization from a large model, then we fine-tune the model using data from a study of human programmer who were asked to write code summaries. We find that our approach outperforms GPT-4 on this task.