  Release notes are admitted as an essential document by practitioners. They
contain the summary of the source code changes for the software releases, such
as issue fixes, added new features, and performance improvements. Manually
producing release notes is a time-consuming and challenging task. For that
reason, sometimes developers neglect to write release notes. For example, we
collect data from GitHub with over 1,900 releases, among them 37% of the
release notes are empty. We propose an automatic generate release notes
approach based on the commit messages and merge pull-request (PR) titles to
mitigate this problem. We implement one of the popular extractive text
summarization techniques, i.e., the TextRank algorithm. However, accurate
keyword extraction is a vital issue in text processing. The keyword matching
and topic extraction process of the TextRank algorithm ignores the semantic
similarity among texts. To improve the keyword extraction method, we integrate
the GloVe word embedding technique with TextRank. We develop a dataset with
1,213 release notes (after null filtering) and evaluate the generated release
notes through the ROUGE metric and human evaluation. We also compare the
performance of our technique with another popular extractive algorithm, latent
semantic analysis (LSA). Our evaluation results show that the improved TextRank
method outperforms LSA.
