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1 Parent(s): adcb90e
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  1. harim_plus.py +8 -3
harim_plus.py CHANGED
@@ -30,11 +30,16 @@ _CITATION = """\
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  }
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  """
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- _DESCRIPTION = f"""HaRiM+ is a reference-less evaluation metric (i.e. requires only article-summary pair, no reference summary) for summarization which hurls the power of summarization model.
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  Summarization model inside the HaRiM+ will read and evaluate how good the quality of a summary given the paired article.
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  It will work great for ranking the summary-article pairs according to its quality.
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  HaRiM+ is proved effective for benchmarking summarization systems (system-level performance) as well as ranking the article-summary pairs (segment-level performance) in comprehensive aspect such as factuality, consistency, coherency, fluency, and relevance. For details, refer to our [paper]({PAPER_URL}) published in AACL2022.
 
 
 
 
 
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  """
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  _KWARGS_DESCRIPTION = """
@@ -89,8 +94,8 @@ class Harimplus(evaluate.Metric):
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  inputs_description=_KWARGS_DESCRIPTION,
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  features=datasets.Features(
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  {
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- "predictions (summaries)": datasets.Value("string", id="sequence"),
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- "references (articles)": datasets.Value("string", id="sequence"),
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  }
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  ),
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  codebase_urls=[CODEBASE_URL],
 
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  }
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  """
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+ _DESCRIPTION = f"""**HaRiM+** is a reference-less evaluation metric (i.e. requires only article-summary pair, no reference summary) for summarization which hurls the power of summarization model.
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  Summarization model inside the HaRiM+ will read and evaluate how good the quality of a summary given the paired article.
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  It will work great for ranking the summary-article pairs according to its quality.
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  HaRiM+ is proved effective for benchmarking summarization systems (system-level performance) as well as ranking the article-summary pairs (segment-level performance) in comprehensive aspect such as factuality, consistency, coherency, fluency, and relevance. For details, refer to our [paper]({PAPER_URL}) published in AACL2022.
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+
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+ NOTE that for HaRiM+...
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+ * predictions = summaries (List[str])
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+ * references = articles (List[str])
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+
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  """
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  _KWARGS_DESCRIPTION = """
 
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  inputs_description=_KWARGS_DESCRIPTION,
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  features=datasets.Features(
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  {
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+ "predictions": datasets.Value("string", id="sequence"),
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+ "references": datasets.Value("string", id="sequence"),
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  }
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  ),
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  codebase_urls=[CODEBASE_URL],