[examples/summarization] deal with `max_length` and `num_beams`
Author: bofenghuang
Pull request: yes

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Hello 👋,

1. In the examples/pytorch/summarization/run_summarization.py, when the `generation_max_length` is not defined, the parameter `max_length` for `generate` function will be set to `val_max_target_length` in the line [675](https://github.com/huggingface/transformers/blob/82e61f34451dbea2de8d2220d51b0609d605dfd7/examples/pytorch/summarization/run_summarization.py#L675-L679), and be used for the **final evaluation** and prediction after training. However, the `max_length` used for the **evaluation during the training** will be possibly set to `None` or the `max_length` defined in the model's config. It's not a consistent behavior. Here I tried to unify this parameter before the `Seq2SeqTrainer` is initialized. Also applied the same to `num_beams`.

2. In the examples/pytorch/summarization/run_summarization_no_trainer.py, fixed the parameter `max_length`. Think it has higher priority than `val_max_target_length`.

Please ignore this PR if it's intended behavior :)

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Comments:
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Hi @sgugger, just removed the `max_length`!

I also added another modif which separates the preprocessing of the training and the validation set in order to truncate the outputs by `max_target_length` and `val_max_target_length` respectively