add data loading script

SciDuet.py

@@ -0,0 +1,168 @@
+import json
+import os
+
+import datasets
+
+_CITATION = """\
+@inproceedings{sun-etal-2021-d2s,
+    title = "{D}2{S}: Document-to-Slide Generation Via Query-Based Text Summarization",
+    author = "Sun, Edward  and
+      Hou, Yufang  and
+      Wang, Dakuo  and
+      Zhang, Yunfeng  and
+      Wang, Nancy X. R.",
+    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = June,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.naacl-main.111",
+    doi = "10.18653/v1/2021.naacl-main.111",
+    pages = "1405--1418",
+}
+"""
+
+_DESCRIPTION = """\
+SciDuet is the first publicaly available dataset for the challenging task of document2slides generation,
+The dataset integrated into GEM is the ACL portion of the whole dataset described in "https://aclanthology.org/2021.naacl-main.111.pdf".
+It contains the full Dev and Test sets, and a portion of the Train dataset. 
+We additionally create a challenge dataset in which the slide titles do not match with the 
+section headers of the corresponding paper.
+Note that although we cannot release the whole training dataset due to copyright issues, researchers can still 
+use our released data procurement code from https://github.com/IBM/document2slides
+to generate the training dataset from the online ICML/NeurIPS anthologies. 
+In the released dataset, the original papers and slides (both are in PDF format) are carefully processed by a combination of PDF/Image processing tookits.
+The text contents from multiple slides that correspond to the same slide title are mreged. 
+"""
+
+_URL = "https://huggingface.co/datasets/GEM/SciDuet/"
+_URLs = {
+    "train": "train.json",
+    "validation": "validation.json",
+    "test": "test.json",
+    "challenge_set": "challenge_woSectionHeader.json",
+}
+
+
+class SciDuetConfig(datasets.BuilderConfig):
+    """BuilderConfig for SciDuet."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SciDuet.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SciDuetConfig, self).__init__(**kwargs)
+
+
+class SciDuet(datasets.GeneratorBasedBuilder):
+    VERSION_1 = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        SciDuetConfig(name="gem_data_split", version=VERSION_1, description="SciDuet - GEM version 1"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "gem_data_split"
+
+    def _info(self):
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "gem_id": datasets.Value("string"),
+                    "paper_id": datasets.Value("string"),
+                    "paper_title": datasets.Value("string"),
+                    "paper_abstract": datasets.Value("string"),
+                    "paper_content": datasets.Features.Sequence({
+                            "paper_content_id": datasets.Value("int32"),
+                            "paper_content_text": datasets.Value("string"),
+                        }),
+                    "paper_headers": datasets.Features.Sequence({
+                            "paper_header_number": datasets.Value("string"),
+                            "paper_header_content": datasets.Value("string"),
+                        }),
+
+                    "slide_id": datasets.Value("string"),
+                    "slide_title": datasets.Value("string"),
+                    "slide_content_text": datasets.Value("string"),
+
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            license="Apache License 2.0",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls_to_download = _URLS[self.config.name]
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": downloaded_files["train"],
+                        "split": "train",
+                        },
+                    ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": downloaded_files["validation"],
+                        "split": "validation",
+                        },
+                    ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": downloaded_files["test"],
+                        "split": "test",
+                        },
+                    ),
+            ] + [
+            datasets.SplitGenerator(
+                name="challenge_woSectionHeader",
+                gen_kwargs={
+                    "filepath": downloaded_files["challenge_set"],
+                    "split": "challenge_woSectionHeader",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)["data"]
+            for i in data:
+                gem_id = data[i]["gem_id"]
+                paper_id = data[i]["paper_id"]
+                paper_title = data[i]["paper_title"]
+                paper_abstract = data[i]["paper"]["abstract"]
+                paper_content_ids = [text["id"] for text in data[i]["paper"]["text"]]
+                paper_content_texts = [text["string"] for text in data[i]["paper"]["text"]]
+                paper_header_numbers = [header["n"] for header in data[i]["paper"]["headers"]]
+                paper_header_contents = [header["section"] for header in data[i]["paper"]["headers"]]
+                for j in data[i]["slides"]:
+                    id_ = gem_id + "#" + "paper-" + paper_id + "#" + "slide-" + str(j)
+                    slide_title = data[i]["slides"][j]["title"]
+                    slide_content_text = '\n'.join(data[i]["slides"][j]["text"])
+
+                yield id_, {
+                    "gem_id": gem_id,
+                    "paper_id": paper_id,
+                    "paper_title": paper_title,
+                    "paper_abstract": paper_abstract,
+                    "paper_content": {"paper_content_id":paper_content_ids, "paper_content_text":paper_content_texts},
+                    "paper_header": {"paper_header_number": paper_header_numbers, "paper_header_content": paper_header_contents},
+
+                    "slide_title": slide_title,
+                    "slide_content_text": slide_content_text,
+                }
+
+
+
+