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API documentation is often the most trusted resource for programming. Manyapproaches have been proposed to augment API documentation by summarizingcomplementary information from external resources such as Stack Overflow.Existing extractive-based summarization approaches excel in producing faithfulsummaries that accurately represent the source content without input lengthrestrictions. Nevertheless, they suffer from inherent readability limitations.On the other hand, our empirical study on the abstractive-based summarizationmethod, i.e., GPT-4, reveals that GPT-4 can generate coherent and concisesummaries but presents limitations in terms of informativeness andfaithfulness.  We introduce APIDocBooster, an extract-then-abstract framework thatseamlessly fuses the advantages of both extractive (i.e., enabling faithfulsummaries without length limitation) and abstractive summarization (i.e.,producing coherent and concise summaries). APIDocBooster consists of twostages: (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 multiplesources into API documentation sections. UPSUM first generates extractivesummaries distinct from the original API documentation and then generatesabstractive summaries guided by extractive summaries through in-contextlearning.  To enable automatic evaluation of APIDocBooster, we construct the firstdataset for API document augmentation. Our automatic evaluation results revealthat each stage in APIDocBooster outperforms its baselines by a large margin.Our human evaluation also demonstrates the superiority of APIDocBooster overGPT-4 and shows that it improves informativeness, relevance, and faithfulnessby 13.89\%, 15.15\%, and 30.56\%, respectively.

APIDocBooster: An Extract-Then-Abstract Framework Leveraging Large Language Models for Augmenting API Documentation