Usually, programming languages have official documentation to guide developers with APIs, methods, and classes. However, researchers identified insufficient or inadequate documentation examples and flaws with the API's complex structure as barriers to learning an API. As a result, developers may consult other sources (StackOverflow, GitHub, etc.) to learn more about an API. Recent research studies have shown that unofficial documentation is a valuable source of information for generating code summaries. We, therefore, have been motivated to leverage such a type of documentation along with deep learning techniques towards generating high-quality summaries for APIs discussed in informal documentation. This paper proposes an automatic approach using the BART algorithm, a state-of-the-art transformer model, to generate summaries for APIs discussed in StackOverflow. We built an oracle of human-generated summaries to evaluate our approach against it using ROUGE and BLEU metrics which are the most widely used evaluation metrics in text summarization. Furthermore, we evaluated our summaries empirically against a previous work in terms of quality. Our findings demonstrate that using deep learning algorithms can improve summaries' quality and outperform the previous work by an average of %57 for Precision, %66 for Recall, and %61 for F-measure, and it runs 4.4 times faster.

本文提出了一种使用BART算法的自动方法，通过深度学习技术，在StackOverflow上生成API的摘要，并使用ROUGE和BLEU指标对其进行评估，结果表明使用深度学习算法可以提高摘要的质量，并且在精确率、召回率和F-measure上表现出色，且运行速度为之前的工作的4.4倍。

利用深度学习对非官方文档进行抽象代码摘要