def preprocess(self, input_data_list, oracle_mode="greedy", selections=3):
        """ Preprocess multiple data points.

           Args:
              input_data_list (SummarizationDataset): The dataset to be preprocessed.
              oracle_mode (str, optional): Sentence selection method.
                Defaults to "greedy".
              selections (int, optional): The number of sentence used as summary.
                Defaults to 3.

            Returns:
                Iterator of dictory objects containing input ids, segment ids,
                sentence class ids, labels, source text and target text.
                If targets is None, the label and target text are None.
        """
        preprocess = functools.partial(
            preprocess_single_add_oracleids, oracle_mode="greedy", selections=3
        )
        return parallel_preprocess(input_data_list, preprocess)
 Preprocess multiple data points.

           Args:
              input_data_list (SummarizationDataset): The dataset to be preprocessed.
              oracle_mode (str, optional): Sentence selection method.
                Defaults to "greedy".
              selections (int, optional): The number of sentence used as summary.
                Defaults to 3.

            Returns:
                Iterator of dictory objects containing input ids, segment ids,
                sentence class ids, labels, source text and target text.
                If targets is None, the label and target text are None.
        
utils_nlp/models/transformers/extractive_summarization.py