Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder
   model, such as Bart or T5.
Define the article that should be summarized.
Add the T5 specific prefix "summarize: ".
Use the PreTrainedModel.generate() method to generate the summary.
In this example we use Google's T5 model. Even though it was pre-trained only on a multi-task mixed dataset (including
CNN / Daily Mail), it yields very good results.
```py
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
tokenizer = AutoTokenizer.from_pretrained("t5-base")
T5 uses a max_length of 512 so we cut the article to 512 tokens.
inputs = tokenizer("summarize: " + ARTICLE, return_tensors="pt", max_length=512, truncation=True)
