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README.md

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----
-language:
-- en
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-task_categories:
-- summarization
-- text-generation
-task_ids: []
-tags:
-- conditional-text-generation
----
-
-# GovReport dataset for summarization
-
-Dataset for summarization of long documents.\
-Adapted from this [repo](https://github.com/luyang-huang96/LongDocSum) and this [paper](https://arxiv.org/pdf/2104.02112.pdf)\
-This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable:
-```python
-"ccdv/govreport-summarization": ("report", "summary")
-```
-
-### Data Fields
-
-- `id`: paper id
-- `report`: a string containing the body of the report
-- `summary`: a string containing the summary of the report
-
-### Data Splits
-
-This dataset has 3 splits: _train_, _validation_, and _test_. \
-Token counts with a RoBERTa tokenizer.
-
-| Dataset Split | Number of Instances |     Avg. tokens       |
-| ------------- | --------------------|:----------------------|
-| Train         | 17,517              |    < 9,000 / < 500    |
-| Validation    | 973                 |    < 9,000 / < 500    |
-| Test          | 973                 |    < 9,000 / < 500    |
-
-
-# Cite original article
-```
-@misc{huang2021efficient,
-      title={Efficient Attentions for Long Document Summarization}, 
-      author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang},
-      year={2021},
-      eprint={2104.02112},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-    }
-```
-

valid.zip → document/govreport-summarization-test.parquet

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train.zip → document/govreport-summarization-train-00000-of-00002.parquet

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document/govreport-summarization-train-00001-of-00002.parquet

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test.zip → document/govreport-summarization-validation.parquet

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govreport-summarization.py

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-import json
-import os
-import datasets
-from datasets.tasks import TextClassification
-
-_DESCRIPTION = """
- GovReport dataset for summarization.
- From paper: Efficient Attentions for Long Document Summarization" by L. Huang et al.
- See: https://arxiv.org/pdf/2104.02112.pdf 
- See: https://github.com/luyang-huang96/LongDocSum
-"""
-_CITATION = """\
-    @misc{huang2021efficient,
-      title={Efficient Attentions for Long Document Summarization}, 
-      author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang},
-      year={2021},
-      eprint={2104.02112},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-    }
-}
-"""
-_ABSTRACT = "summary"
-_ARTICLE = "report"
-
-class GovReportSummarizationConfig(datasets.BuilderConfig):
-    """BuilderConfig for GovReportSummarization."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for GovReportSummarization.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(GovReportSummarizationConfig, self).__init__(**kwargs)
-
-
-class GovReportSummarizationDataset(datasets.GeneratorBasedBuilder):
-    """GovReportSummarization Dataset."""
-    
-    _TRAIN_FILE = "train.zip"
-    _VAL_FILE = "valid.zip"
-    _TEST_FILE = "test.zip"
-
-    BUILDER_CONFIGS = [
-        GovReportSummarizationConfig(
-            name="document",
-            version=datasets.Version("1.0.0"),
-            description="GovReport dataset for summarization, document",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "document"
-
-    def _info(self):
-        # Should return a datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    _ARTICLE: datasets.Value("string"),
-                    _ABSTRACT: datasets.Value("string"),
-                    #"id": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/luyang-huang96/LongDocSum",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        train_path = os.path.join(dl_manager.download_and_extract(self._TRAIN_FILE), "train.txt")
-        val_path = os.path.join(dl_manager.download_and_extract(self._VAL_FILE), "valid.txt")
-        test_path = os.path.join(dl_manager.download_and_extract(self._TEST_FILE), "test.txt")
-        
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
-            ),
-        ]
-    
-    def _generate_examples(self, filepath):
-        """Generate GovReportSummarization examples."""
-        with open(filepath, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
-                data = json.loads(row)
-                report = data["report"]
-                summary = data["summary"]
-
-                yield id_, {"report": report, "summary": summary}