Automating API Documentation with LLMs: A BERTopic Approach
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Abstract (arXiv)
Developers rely on API documentation, but official sources are often lengthy, complex, or incomplete. Many turn to community-driven forums like Stack Overflow for practical insights. We propose automating the summarization of informal sources, focusing on Android APIs. Using BERTopic, we extracted prevalent topics from 3.6 million Stack Overflow posts and applied extractive summarization techniques to generate concise summaries, including code snippets. A user study with 30 Android developers assessed the summaries for coherence, relevance, informativeness, and satisfaction, showing improved productivity. Integrating formal API knowledge with community-generated content enhances documentation, making API resources more accessible and actionable work.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 6 Sep 2025.
Builds on
7 of 13 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
- 2019Leveraging BERT for Extractive Text Summarization on Lecturescited by 63
- 2021Leveraging Unsupervised Learning to Summarize APIs Discussed in Stack Overflowcited by 9
- 2023Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Studycited by 7
- 2022Towards Code Summarization of APIs Based on Unofficial Documentation Using NLP Techniquescited by 4
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Citing papers per year: 2026: 1.
1 paper.