config and copied code from cnn/dailymail

citesum.py

@@ -0,0 +1,129 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""CiteSum dataset"""
+
+import hashlib
+import os
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_HOMEPAGE = "https://github.com/morningmoni/CiteSum"
+
+_DESCRIPTION = """\
+Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptation
+CiteSum contains TLDR summaries for scientific papers from their citation texts without human annotation.
+CiteSum is around 30 times larger than the previous human-curated dataset SciTLDR.
+"""
+
+# The second citation introduces the source data, while the first
+# introduces the specific form (non-anonymized) we use here.
+_CITATION = """\
+@misc{https://doi.org/10.48550/arxiv.2205.06207,
+  doi = {10.48550/ARXIV.2205.06207},
+  url = {https://arxiv.org/abs/2205.06207},
+  author = {Mao, Yuning and Zhong, Ming and Han, Jiawei},
+  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {CiteSum: Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptation},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+
+"""
+
+_DOWNLOAD_URL = "https://drive.google.com/file/d/1ndHCREXGSPnDUNllladh9qCtayqbXAfJ"
+
+
+class CiteSumConfig(datasets.BuilderConfig):
+    """BuilderConfig for CiteSum."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for CiteSum.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(**kwargs)
+
+
+class CiteSum(datasets.GeneratorBasedBuilder):
+    """CiteSum summarization dataset."""
+
+    BUILDER_CONFIGS = [CiteSumConfig(name="citesum", description="Plain text")]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "src": datasets.Value("string"),
+                    "tgt": datasets.Value("string"),
+                    "paper_id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "discipline": {
+                        "venue": datasets.Value("string"),
+                        "journal": datasets.Value("string"),
+                        "mag_field_of_study": datasets.features.Sequence(
+                            datasets.Value("string")
+                        ),
+                    },
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_paths = dl_manager.download(_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={
+                    "urls_file": dl_paths[split],
+                    "files_per_archive": [
+                        dl_manager.iter_archive(dl_paths["cnn_stories"]),
+                        dl_manager.iter_archive(dl_paths["dm_stories"]),
+                    ],
+                },
+            )
+            for split in [
+                datasets.Split.TRAIN,
+                datasets.Split.VALIDATION,
+                datasets.Split.TEST,
+            ]
+        ]
+
+    def _generate_examples(self, urls_file, files_per_archive):
+        urls = _get_url_hashes(urls_file)
+        idx = 0
+        for files in files_per_archive:
+            for path, file in files:
+                hash_from_path = _get_hash_from_path(path)
+                if hash_from_path in urls:
+                    article, highlights = _get_art_abs(file, self.config.version)
+                    if not article or not highlights:
+                        continue
+                    yield idx, {
+                        _ARTICLE: article,
+                        _HIGHLIGHTS: highlights,
+                        "id": hash_from_path,
+                    }
+                    idx += 1