builder script

mediasum.py

@@ -0,0 +1,117 @@
+# 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
+"""MediaSum dataset"""
+
+import os
+import json
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_HOMEPAGE = "https://github.com/zcgzcgzcg1/MediaSum"
+
+_DESCRIPTION = """\
+This large-scale media interview dataset contains 463.6K transcripts with abstractive summaries, 
+collected from interview transcripts and overview / topic descriptions from NPR and CNN.
+"""
+
+_CITATION = """\
+@article{zhu2021mediasum,
+  title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization},
+  author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael},
+  journal={arXiv preprint arXiv:2103.06410},
+  year={2021}
+}
+"""
+
+_DOWNLOAD_URLS = {
+    "train": "https://huggingface.co/datasets/nbroad/mediasum/resolve/main/train.json",
+    "validation": "https://huggingface.co/datasets/nbroad/mediasum/resolve/main/validation.json",
+    "test": "https://huggingface.co/datasets/nbroad/mediasum/resolve/main/test.json",
+}
+
+
+class MediaSumConfig(datasets.BuilderConfig):
+    """BuilderConfig for MediaSum."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for MediaSum.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(**kwargs)
+
+
+class MediaSum(datasets.GeneratorBasedBuilder):
+    """MediaSum summarization dataset."""
+
+    BUILDER_CONFIGS = [MediaSumConfig(name="mediasum", description="Plain text")]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "program": datasets.Value("string"),
+                    "date": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "summary": datasets.Value("string"),
+                    "utt": datasets.features.Sequence(
+                            datasets.Value("string")
+                        ),
+                    "speaker": datasets.features.Sequence(
+                            datasets.Value("string")
+                        ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_path = dl_manager.download(_DOWNLOAD_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={
+                    "filepath": dl_path[split],
+                },
+            )
+            for split in [
+                datasets.Split.TRAIN,
+                datasets.Split.VALIDATION,
+                datasets.Split.TEST,
+            ]
+        ]
+
+    def _generate_examples(self, filepath):
+
+        with open(filepath, "r") as fp:
+            for idx, line in enumerate(fp):
+                data = json.loads(line)
+                
+                # Some do not have titles
+                if "title" not in data:
+                    data["title"] = ""
+                yield idx, data