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crosssum.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """
+@inproceedings{bhattacharjee-etal-2023-crosssum,
+  author    = {Bhattacharjee, Abhik and Hasan, Tahmid and Ahmad, Wasi Uddin and Li, Yuan-Fang and Kang, Yong-Bin and Shahriyar, Rifat},
+  title     = {CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs},
+  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics},
+  publisher = {Association for Computational Linguistics},
+  year      = {2023},
+  url       = {https://aclanthology.org/2023.acl-long.143},
+  doi       = {10.18653/v1/2023.acl-long.143},
+  pages     = {2541--2564},
+  }
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ind", "mya", "vie"]
+_DATASETNAME = "crosssum"
+_DESCRIPTION = """
+This is a large-scale cross-lingual summarization dataset containing article-summary samples in 1,500+ language pairs,
+including pairs with the Burmese, Indonesian and Vietnamese languages. Articles in the first language are assigned
+summaries in the second language.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/csebuetnlp/CrossSum"
+_LICENSE = Licenses.CC_BY_NC_SA_4_0.value
+_URL = "https://huggingface.co/datasets/csebuetnlp/CrossSum"
+
+
+_SUPPORTED_TASKS = [Tasks.CROSS_LINGUAL_SUMMARIZATION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class CrossSumDataset(datasets.GeneratorBasedBuilder):
+    """Dataset of cross-lingual article-summary samples."""
+
+    SUBSETS = [
+        "ind_mya",
+        "ind_vie",
+        "mya_ind",
+        "mya_vie",
+        "vie_mya",
+        "vie_ind",
+    ]
+    LANG_CODE_MAPPER = {"ind": "indonesian", "mya": "burmese", "vie": "vietnamese"}
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema for {subset} subset",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in SUBSETS
+    ] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_seacrowd_t2t",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema for {subset} subset",
+            schema="seacrowd_t2t",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in SUBSETS
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_ind_mya_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "source_url": datasets.Value("string"),
+                    "target_url": datasets.Value("string"),
+                    "summary": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_t2t":
+            features = schemas.text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        # dl_manager not used since dataloader uses HF 'load_dataset'
+        return [
+            datasets.SplitGenerator(name=split, gen_kwargs={"split": split._name})
+            for split in (
+                datasets.Split.TRAIN,
+                datasets.Split.VALIDATION,
+                datasets.Split.TEST,
+            )
+        ]
+
+    def _load_hf_data_from_remote(self, split: str) -> datasets.DatasetDict:
+        """Load dataset from HuggingFace."""
+        source_lang = self.LANG_CODE_MAPPER[self.config.subset_id.split("_")[-2]]
+        target_lang = self.LANG_CODE_MAPPER[self.config.subset_id.split("_")[-1]]
+        HF_REMOTE_REF = "/".join(_URL.split("/")[-2:])
+        _hf_dataset_source = datasets.load_dataset(HF_REMOTE_REF, f"{source_lang}-{target_lang}", split=split)
+        return _hf_dataset_source
+
+    def _generate_examples(self, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        data = self._load_hf_data_from_remote(split)
+        for index, row in enumerate(data):
+            if self.config.schema == "source":
+                example = row
+            elif self.config.schema == "seacrowd_t2t":
+                example = {"id": str(index), "text_1": row["text"], "text_2": row["summary"], "text_1_name": "document", "text_2_name": "summary"}
+            yield index, example