  Source code summarization is the task of creating short, natural language
descriptions of source code. Code summarization is the backbone of much
software documentation such as JavaDocs, in which very brief comments such as
"adds the customer object" help programmers quickly understand a snippet of
code. In recent years, automatic code summarization has become a high value
target of research, with approaches based on neural networks making rapid
progress. However, as we will show in this paper, the production of good
summaries relies on the production of the action word in those summaries: the
meaning of the example above would be completely changed if "removes" were
substituted for "adds." In this paper, we advocate for a special emphasis on
action word prediction as an important stepping stone problem towards better
code summarization -- current techniques try to predict the action word along
with the whole summary, and yet action word prediction on its own is quite
difficult. We show the value of the problem for code summaries, explore the
performance of current baselines, and provide recommendations for future
research.
