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 :obj:`List[str]`):
                One or several articles (or one list of articles) to summarize.
            return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not to include the decoded texts in the outputs
            return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to include the tensors of predictions (as token indices) in the outputs.
            clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`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.html#generative-models>`__).

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

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