first

.gitattributes

@@ -25,3 +25,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+val.zip filter=lfs diff=lfs merge=lfs -text
+vocab.zip filter=lfs diff=lfs merge=lfs -text
+test.zip filter=lfs diff=lfs merge=lfs -text
+train.zip filter=lfs diff=lfs merge=lfs -text

pubmed-summarization.py

@@ -0,0 +1,121 @@
+import json
+import os
+
+import datasets
+from datasets.tasks import TextClassification
+
+_CITATION = None
+
+
+_DESCRIPTION = """
+ PubMed dataset for summarization.
+ From paper: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents" by A. Cohan et al.
+ See: https://aclanthology.org/N18-2097.pdf 
+ See: https://github.com/armancohan/long-summarization
+"""
+_CITATION = """\
+    @inproceedings{cohan-etal-2018-discourse,
+    title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
+    author = "Cohan, Arman  and
+      Dernoncourt, Franck  and
+      Kim, Doo Soon  and
+      Bui, Trung  and
+      Kim, Seokhwan  and
+      Chang, Walter  and
+      Goharian, Nazli",
+    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
+    month = jun,
+    year = "2018",
+    address = "New Orleans, Louisiana",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/N18-2097",
+    doi = "10.18653/v1/N18-2097",
+    pages = "615--621",
+    abstract = "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.",
+}
+"""
+_ABSTRACT = "abstract"
+_ARTICLE = "article"
+
+class PubMedSummarizationConfig(datasets.BuilderConfig):
+    """BuilderConfig for PatentClassification."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for PubMedSummarization.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(PubMedSummarizationConfig, self).__init__(**kwargs)
+
+
+class PubMedSummarizationDataset(datasets.GeneratorBasedBuilder):
+    """PubMedSummarization Dataset."""
+    
+    _DOWNLOAD_URL = "https://huggingface.co/datasets/ccdv/pubmed-summarization/resolve/main/"
+    _TRAIN_FILE = "train.zip"
+    _VAL_FILE = "val.zip"
+    _TEST_FILE = "test.zip"
+
+    BUILDER_CONFIGS = [
+        PubMedSummarizationConfig(
+            name="pubmed",
+            version=datasets.Version("1.0.0"),
+            description="PubMed dataset for summarization",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "pubmed"
+
+    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/armancohan/long-summarization",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = dl_manager.download_and_extract(self._DOWNLOAD_URL + self._TRAIN_FILE)
+        val_path = dl_manager.download_and_extract(self._DOWNLOAD_URL + self._VAL_FILE)
+        test_path = dl_manager.download_and_extract(self._DOWNLOAD_URL + self._TEST_FILE)
+
+        #train_path = dl_manager.download_and_extract(self._TRAIN_FILE)
+        #val_path = dl_manager.download_and_extract(self._VAL_FILE)
+        #test_path = dl_manager.download_and_extract(self._TEST_FILE)
+        
+        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 PubMedSummarization examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+
+                """
+                'article_id': str,
+                'abstract_text': List[str],
+                'article_text': List[str],
+                'section_names': List[str],
+                'sections': List[List[str]]
+                """
+                article = data["article"]
+                abstract = data["abstract"]
+                yield id_, {"article": ' '.join(article), "abstract": ' '.join(abstract)}

test.zip

@@ -0,0 +1,3 @@
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+oid sha256:fa6666b57d2335a1962f2d8a8511a7bf5f6e457215323645be62457ce8bbfcdf
+size 43787908

train.zip

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+oid sha256:988355271552520ad30fab4c2d63a3ef8d985a179e30089da766ee04ec017a10
+size 779257354

val.zip

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+oid sha256:37a0b6b2c2f9b3fc8296f2d244ec813664571e7ef5bec8cf015626c83e485460
+size 43705498

vocab.zip

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+oid sha256:5d25daab57cafba29ff14d3ecd45bdf8d0a3fa882426391f61a891f0817b7a73
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