How to conclude the generality of any classification methods?
You're experiencing an unfortunately common issue with the current state of system/model evaluation. In addition to evaluating on different datasets, authors often leave out important details, such as the procedure for hyperparameter tuning, detailed evaluation metrics (i.e. true positives, false negatives, etc. in addition to F-score), and ablation analyses. In cases like these, we cannot conclude that one method is necessarily better than the others or state-of-the-art.
The best way to estimate the generality of each method when the literature has so far failed to do so is to implement each yourself and do a fair comparative evaluation. You would evaluate all methods on the same dataset with the same pre-processing steps and hyperparameter tuning procedure and, if possible, introduce additional evaluation datasets. It can also be very enlightening to perform an ablation analysis in which you iteratively remove certain components of the methods and re-evaluate to see how much of a performance hit you take.
Doing the above and communicating it (via a publication, blog post, or whatever) will not only help you, but everyone else working in the area.