# Towards Code Summarization of APIs Based on Unofficial Documentation Using NLP Techniques

arXiv: https://arxiv.org/abs/2208.06318 · HTML: https://mlsanity.com/paper/2208.06318/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 2 of 2)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Leveraging Unsupervised Learning to Summarize APIs Discussed in Stack Overflow](https://mlsanity.com/paper/2111.13962/) (2021)

## Built on by (most cited first, 4 of 4)

- [Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Study](https://mlsanity.com/paper/2305.03017/) (2023)
- [Enhancing API Documentation through BERTopic Modeling and Summarization](https://mlsanity.com/paper/2308.09070/) (2023)
- [Automating API Documentation with LLMs: A BERTopic Approach](https://mlsanity.com/paper/2509.05749/) (2025)
- [Leveraging Deep Learning for Abstractive Code Summarization of Unofficial Documentation](https://mlsanity.com/paper/2310.15015/) (2023)

## Abstract (arXiv)

Each programming language comes with official documentation to guide developers with APIs, methods, and classes. However, in some cases, official documentation is not an efficient way to get the needed information. As a result, developers may consult other sources (e.g., Stack Overflow, GitHub) to learn more about an API, its implementation, usage, and other information that official documentation may not provide. In this research, we propose an automatic approach to generate summaries for APIs and methods by leveraging unofficial documentation using NLP techniques. Our findings demonstrate that the generated summaries are competitive, and can be used as a complementary source for guiding developers in software development and maintenance tasks.