class_docstr: Scenario for single document text summarization.
  Currently, supports the following datasets:
  1. BillSum (https://aclanthology.org/D19-5406/)
  2. MultiLexSum (https://arxiv.org/abs/2206.10883)
  3. EurLexSum (https://arxiv.org/abs/2210.13448)
  
  Task prompt structure
  
      Summarize the given document.
      Document: {tok_1 ... tok_n}
      Summary: {tok_1 ... tok_m}
  
  Example from MultiLexSum dataset (Short to Tiny)
  
      Document: {This case is about an apprenticeship test that had a disparate impact
                  on Black apprenticeship applicants. The Equal Employment Opportunity
                  Commission (EEOC) filed this lawsuit on December 27, 2004, in U.S.
                  District Court for the Southern District of Ohio. Filing on behalf
                  of thirteen Black individuals and a class of similarly situated Black
                  apprenticeship test takers, the EEOC alleged that the individuals’
                  employer, the Ford Motor Company, as well as their union, the United
                  Automobile, Aerospace, and Agricultural implement workers of America
                  (the “UAW”), and the Ford-UAW Joint Apprenticeship Committee, violated
                  Title VII of the Civil Rights Act, 42 U.S.C. § 1981, and Michigan state
                  anti-discrimination law. The EEOC sought injunctive relief and damages
                  for the Black apprenticeship applicants. The individuals also brought a
                  separate class action against Ford and the UAW, and the cases were
                  consolidated. In June 2005, both cases were resolved via a class
                  settlement agreement. Ford agreed to pay $8.55 million and to implement
                  a new selection process for its apprenticeship programs, and the court
                  ordered Ford to cover attorneys’ fees and expenses. This case is closed.}
      Summary: {2005 class action settlement resulted in Ford paying $8.55m to redesign
                  its selection process for apprenticeship programs to address the
                  previous process’s disparate impact on Black applicants.}
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code: def _load_dataset(self, dataset_name: str):
      if dataset_name == "BillSum":
          hf_name = "billsum"
          dataset = self._download_dataset(hf_name)
          article_key = "text"
          summary_key = "summary"
      elif dataset_name == "MultiLexSum":
          hf_name = "allenai/multi_lexsum"
          dataset = self._download_dataset(hf_name)
          article_key = "summary/long"
          summary_key = "summary/short"  # we could do summary/tiny additionally here
      elif dataset_name == "EurLexSum":
          hf_name = "dennlinger/eur-lex-sum"
          dataset = self._download_dataset(hf_name)
          article_key = "reference"
          summary_key = "summary"
      else:
          raise ValueError("The specified dataset is not supported")
  
      return dataset, article_key, summary_key
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/legal_summarization_scenario.py
imports: from pathlib import Path
  from typing import List, Optional, Any
  import datasets
  from datasets import load_dataset
  from .scenario import Scenario, Instance, Reference, TRAIN_SPLIT, VALID_SPLIT, TEST_SPLIT, CORRECT_TAG, Input, Output
label_desc: Use image node_id 3 for calling the LegalSummarizationScenario obj's underlying member method code with example usage: obj._load_dataset(dataset_name) and returns: dataset,  article_key,  summary_key