# Leveraging Unsupervised Learning to Summarize APIs Discussed in Stack Overflow

arXiv: https://arxiv.org/abs/2111.13962 · HTML: https://mlsanity.com/paper/2111.13962/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 27 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [A Transformer-based Approach for Source Code Summarization](https://mlsanity.com/paper/2005.00653/) (2020)
- [Improving Automatic Source Code Summarization via Deep Reinforcement Learning](https://mlsanity.com/paper/1811.07234/) (2018)
- [A Neural Model for Generating Natural Language Summaries of Program Subroutines](https://mlsanity.com/paper/1902.01954/) (2019)
- [Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges](https://mlsanity.com/paper/2002.05442/) (2020)
- [Automatically Generating Commit Messages from Diffs using Neural Machine Translation](https://mlsanity.com/paper/1708.09492/) (2017)
- [Automatic Code Summarization: A Systematic Literature Review](https://mlsanity.com/paper/1909.04352/) (2019)
- [Developer Reading Behavior While Summarizing Java Methods: Size and Context Matters](https://mlsanity.com/paper/1903.03358/) (2019)
- [Mining API Usage Scenarios from Stack Overflow](https://mlsanity.com/paper/2102.08874/) (2021)

## Built on by (most cited first, 9 of 9)

- [Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Study](https://mlsanity.com/paper/2305.03017/) (2023)
- [Towards Code Summarization of APIs Based on Unofficial Documentation Using NLP Techniques](https://mlsanity.com/paper/2208.06318/) (2022)
- [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)
- [Code Compliance Assessment as a Learning Problem](https://mlsanity.com/paper/2209.04602/) (2022)
- [Leveraging Data Mining Algorithms to Recommend Source Code Changes](https://mlsanity.com/paper/2305.00323/) (2023)
- [Leveraging Deep Learning for Abstractive Code Summarization of Unofficial Documentation](https://mlsanity.com/paper/2310.15015/) (2023)
- [Revolutionizing API Documentation through Summarization](https://mlsanity.com/paper/2401.11361/) (2024)
- [ArchISMiner: A Framework for Automatic Mining of Architectural Issue-Solution Pairs from Online Developer Communities](https://mlsanity.com/paper/2510.21966/) (2025)

## Abstract (arXiv)

Automated source code summarization is a task that generates summarized information about the purpose, usage, and--or implementation of methods and classes to support understanding of these code entities. Multiple approaches and techniques have been proposed for supervised and unsupervised learning in code summarization, however, they were mostly focused on generating a summary for a piece of code. In addition, very few works have leveraged unofficial documentation. This paper proposes an automatic and novel approach for summarizing Android API methods discussed in Stack Overflow that we consider as unofficial documentation in this research. Our approach takes the API method's name as an input and generates a natural language summary based on Stack Overflow discussions of that API method. We have conducted a survey that involves 16 Android developers to evaluate the quality of our automatically generated summaries and compare them with the official Android documentation. Our results demonstrate that while developers find the official documentation more useful in general, the generated summaries are also competitive, in particular for offering implementation details, and can be used as a complementary source for guiding developers in software development and maintenance tasks.