TensorFlow: How and why to use SavedModel

I have a few questions regarding the SavedModel API, whose documentation I find leaves a lot of details unexplained.

The first three questions are about what to pass to the arguments of the add_meta_graph_and_variables() method of tf.saved_model.builder.SavedModelBuilder, while the fourth question is about why to use the SavedModel API over tf.train.Saver.

1. What is the format of the signature_def_map argument? Do I normally need to set this argument when saving a model?

2. Similarly, What is the format of the assets_collection argument?

3. Why do you save a list of tags with a metagraph as opposed to just giving it a name (i.e. attaching just one unique tag to it)? Why would I add multiple tags to a given metagraph? What if I try to load a metagrpah from a pb by a certain tag, but multiple metagraphs in that pb match that tag?

4. The documentation argues that it is recommended to use SavedModel to save entire models (as opposed to variables only) in self-contained files. But tf.train.Saver also saves the graph in addition to the variables in a .meta file. So what are the advantages of using SavedModel? The documentation says

When you want to save and load variables, the graph, and the graph's metadata--basically, when you want to save or restore your model--we recommend using SavedModel. SavedModel is a language-neutral, recoverable, hermetic serialization format. SavedModel enables higher-level systems and tools to produce, consume, and transform TensorFlow models.

but this explanation is quite abstract and doesn't really help me understand what the advantages of SavedModel are. What would be concrete examples where SavedModel (as opposed to tf.train.Saver) would be better to use?

Please note that my question is not a duplicate of this question. I'm not asking how to save a model, I am asking very specific questions about the properties of SavedModel, which is only one of multiple mechanisms TensorFlow provides to save and load models. None of the answers in the linked question touch on the SavedModel API (which, once again, is not the same as tf.train.Saver).