  API documentation is often the most trusted resource for programming. Many
approaches have been proposed to augment API documentation by summarizing
complementary information from external resources such as Stack Overflow.
Existing extractive-based summarization approaches excel in producing faithful
summaries that accurately represent the source content without input length
restrictions. Nevertheless, they suffer from inherent readability limitations.
On the other hand, our empirical study on the abstractive-based summarization
method, i.e., GPT-4, reveals that GPT-4 can generate coherent and concise
summaries but presents limitations in terms of informativeness and
faithfulness.
  We introduce APIDocBooster, an extract-then-abstract framework that
seamlessly fuses the advantages of both extractive (i.e., enabling faithful
summaries without length limitation) and abstractive summarization (i.e.,
producing coherent and concise summaries). APIDocBooster consists of two
stages: (1) \textbf{C}ontext-aware \textbf{S}entence \textbf{S}ection
\textbf{C}lassification (CSSC) and (2) \textbf{UP}date \textbf{SUM}marization
(UPSUM). CSSC classifies API-relevant information collected from multiple
sources into API documentation sections. UPSUM first generates extractive
summaries distinct from the original API documentation and then generates
abstractive summaries guided by extractive summaries through in-context
learning.
  To enable automatic evaluation of APIDocBooster, we construct the first
dataset for API document augmentation. Our automatic evaluation results reveal
that each stage in APIDocBooster outperforms its baselines by a large margin.
Our human evaluation also demonstrates the superiority of APIDocBooster over
GPT-4 and shows that it improves informativeness, relevance, and faithfulness
by 13.89\%, 15.15\%, and 30.56\%, respectively.
