src/transformers/pipelines/text2text_generation.py/SummarizationPipeline/__call__
class SummarizationPipeline:    def __call__(self, *args, **kwargs):
        r"""
        Summarize the text(s) given as inputs.

        Args:
            documents (*str* or `List[str]`):
                One or several articles (or one list of articles) to summarize.
            return_text (`bool`, *optional*, defaults to `True`):
                Whether or not to include the decoded texts in the outputs
            return_tensors (`bool`, *optional*, defaults to `False`):
                Whether or not to include the tensors of predictions (as token indices) in the outputs.
            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
                Whether or not to clean up the potential extra spaces in the text output.
            generate_kwargs:
                Additional keyword arguments to pass along to the generate method of the model (see the generate method
                corresponding to your framework [here](./model#generative-models)).

        Return:
            A list or a list of list of `dict`: Each result comes as a dictionary with the following keys:

            - **summary_text** (`str`, present when `return_text=True`) -- The summary of the corresponding input.
            - **summary_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The token
              ids of the summary.
        """
        return super().__call__(*args, **kwargs)