Read between the lines -- Functionality Extraction From READMEs
<|reference_start|>Read between the lines -- Functionality Extraction From READMEs: 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.<|reference_end|>
@article{kumar2024read,
    title={Read between the lines -- Functionality Extraction From READMEs},
    author={Prince Kumar, Srikanth Tamilselvam, Dinesh Garg},
    journal={arXiv preprint arXiv:2403.10205},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10205},
    primaryClass={cs.CL cs.AI}
}