@ttnphns's explanation is excellent. That said, Stata definitely decomposes the information in ways that aren't familiar to me, despite having used MCA for years. One thing that is consistent across all packages is the low dimensional nature of all CA output...two dimensions are always reported. The thing that's missing from this Stata output is the comparable and symmetric output for the rows. Since MCA works on a tabular information, that table is either row or column dominant -- the choice of which one to focus on is the analyst's.
Regarding the terminology, it's too complex for a quick summary (here) except to note that a key insight to understanding these metrics lies in the geometry behind them.
One of the architects of CA is Michael Greenacre who has been publishing seminal works on it since the 80s. His most recent book on this topic is Correspondence Analysis in Practice on Amazon here:
You can access this book online. It contains, in the first chapters, thorough explanations of this terminology as well as of the geometry underlying the decompositions.