Abstract
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.- Anthology ID:
- 2024.findings-naacl.251
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2024
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3977–3990
- Language:
- URL:
- https://aclanthology.org/2024.findings-naacl.251
- DOI:
- Cite (ACL):
- Prince Kumar, Srikanth Tamilselvam, and Dinesh Garg. 2024. Read between the lines - Functionality Extraction From READMEs. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3977–3990, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Read between the lines - Functionality Extraction From READMEs (Kumar et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2024.findings-naacl.251.pdf