Macro F1 result higher than accuracy for imbalanced dataset
They calculated the "standard" F1 score as defined for binary classification tasks:

precision = 0.656
recall = 0.792
f1 = 2 * (precision * recall) / (precision + recall)
f1


gives

0.7176132596685083


Other versions of the F1 score are used for multiple classes as you can see here under "Extension to multi-class classification":


  The F-score is also used for evaluating classification problems with more than two classes (Multiclass classification). In this setup, the final score is obtained by micro-averaging (biased by class frequency) or macro-averaging (taking all classes as equally important). For macro-averaging, two different formulas have been used by applicants: the F-score of (arithmetic) class-wise precision and recall means or the arithmetic mean of class-wise F-scores, where the latter exhibits more desirable properties.


Alternatively, see here for the scikit learn implementation of the F1 score and its parameter description.