Print out summaries in console
Overall, there isn't first class support for your use case in TensorFlow, so I would parse the merged summaries back into a tf.Summary() protocol buffer, and then filter / print data as you see fit. 
If you come up with a nice pattern, you could then merge it back into TensorFlow itself. I could imagine making this an optional setting on the tf.train.SummaryWriter, but it is probably best to just have a separate class for console-printing out interesting summaries.
If you want to encode into the graph itself which items should be summarized and printed, and which items should only be summarized (or to setup a system of different verbosity levels) you could use the Collections argument to the summary op constructors to organize different summaries into different groups. E.g. the loss summary could be put in collections [GraphKeys.SUMMARIES, 'ALWAYS_PRINT'], but another summary could be in collection [GraphKeys.SUMMARIES, 'PRINT_IF_VERBOSE'], etc. Then you can have different merge_summary ops for the different types of printing, and control which ones are run via command line flags.
Tensorflow's scalar/histogram/image_summary functions are very useful for logging data for viewing with tensorboard. But I'd like that information printed to the console as well (e.g. if I'm a crazy person without a desktop environment). 
Currently, I'm adding the information of interest to the fetch list before calling sess.run, but this seems redundant as I'm already fetching the merged summaries. Fetching the merged summaries returns a protobuf, so I imagine I could scrape it using some generic python protobuf library, but this seems like a common enough use case that there should be an easier way.
The main motivation here is encapsulation. Let's stay I have my model and training script in different files. My model has a bunch of calls to tf.scalar_summary for the information that useful to log. Ideally, I'd be able to specify whether or not to additionally print this information to console by changing something in the training script without changing the model file. Currently, I either pass all of the useful information to the training script (so I can fetch them), or I pepper the model file with calls to tf.Print