While text summarization is a well-known NLP task, in this paper, we
introduce a novel and useful variant of it called functionality extraction from
Git README files. Though this task is a text2text generation at an abstract
level, it involves its own peculiarities and challenges making existing
text2text generation systems not very useful. The motivation behind this task
stems from a recent surge in research and development activities around the use
of large language models for code-related tasks, such as code refactoring, code
summarization, etc. We also release a human-annotated dataset called FuncRead,
and develop a battery of models for the task. Our exhaustive experimentation
shows that small size fine-tuned models beat any baseline models that can be
designed using popular black-box or white-box large language models (LLMs) such
as ChatGPT and Bard. Our best fine-tuned 7 Billion CodeLlama model exhibit 70%
and 20% gain on the F1 score against ChatGPT and Bard respectively.

从 Git README 文件中提取功能，通过使用大型语言模型进行文本摘要和代码相关任务的研究，发现小规模微调模型在 F1 得分上击败了 ChatGPT 和 Bard。