  Generating and maintaining API documentation with integrity and consistency
can be time-consuming and expensive for evolving APIs. To solve this problem,
several approaches have been proposed to automatically generate high-quality
API documentation based on a combination of knowledge from different web
sources. However, current researches are weak in handling unpopular APIs and
cannot generate structured API documentation. Hence, in this poster, we propose
a hybrid technique(namely \textit{gDoc}) for the automatic generation of
structured API documentation. We first present a fine-grained search-based
strategy to generate the description for partial API parameters via computing
the relevance between various APIs, ensuring the consistency of API
documentation. Then, we employ the cross-modal pretraining Seq2Seq model M6 to
generate a structured API document for each API, which treats the document
generation problem as a translation problem. Finally, we propose a heuristic
algorithm to extract practical parameter examples from API request logs. The
experiments evaluated on the online system show that this work's approach
significantly improves the effectiveness and efficiency of API document
generation.
