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wikilingua.py

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+import os
+import pickle
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@inproceedings{
+    ladhak-wiki-2020,
+    title={WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization},
+    author={Faisal Ladhak, Esin Durmus, Claire Cardie and Kathleen McKeown},
+    booktitle={Findings of EMNLP, 2020},
+    year={2020}
+}
+"""
+
+_DATASETNAME = "wikilingua"
+
+_DESCRIPTION = """\
+We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of crosslingual abstractive 
+summarization systems. We extract article and summary pairs in 18 languages from WikiHow12, a high quality, 
+collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard 
+article summary alignments across languages by aligning the images that are used to describe each how-to step in an 
+article.
+"""
+
+_HOMEPAGE = "https://github.com/esdurmus/Wikilingua"
+
+_LANGUAGES = ["ind"]
+
+_LICENSE = "CC-BY-NC-SA 3.0"
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://drive.google.com/u/0/uc?id=1PGa8j1_IqxiGTc3SU6NMB38sAzxCPS34&export=download"
+}
+
+_SUPPORTED_TASKS = [Tasks.SUMMARIZATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class Wikilingua(datasets.GeneratorBasedBuilder):
+    """
+    The dataset includes 47,511 articles from WikiHow. Extracted gold-standard article-summary alignments across
+    languages by aligning the images that are used to describe each how-to step in an article.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="wikilingua_source",
+            version=SOURCE_VERSION,
+            description="wikilingua source schema",
+            schema="source",
+            subset_id="wikilingua",
+        ),
+        SEACrowdConfig(
+            name="wikilingua_seacrowd_t2t",
+            version=SEACROWD_VERSION,
+            description="wikilingua Nusantara schema",
+            schema="seacrowd_t2t",
+            subset_id="wikilingua",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "wikilingua_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        features = []
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("int64"),
+                    "link": datasets.Value("string"),
+                    "main_point": datasets.Value("string"),
+                    "summary": datasets.Value("string"),
+                    "document": datasets.Value("string"),
+                    "english_section_name": datasets.Value("string"),
+                    "english_url": 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."""
+
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir),
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+            with open(filepath, "rb") as file:
+                indonesian_docs = pickle.load(file)
+
+            _id = 1
+            for key_link, articles in indonesian_docs.items():
+                for main_point, items in articles.items():
+                    example = {"id": _id, "link": key_link, "main_point": main_point, "summary": items["summary"], "document": items["document"], "english_section_name": items["english_section_name"], "english_url": items["english_url"]}
+                    yield _id, example
+                    _id += 1
+        elif self.config.schema == "seacrowd_t2t":
+            with open(filepath, "rb") as file:
+                indonesian_docs = pickle.load(file)
+
+            _id = 1
+            for key_link, articles in indonesian_docs.items():
+                for main_point, items in articles.items():
+                    example = {"id": _id, "text_1": items["document"], "text_2": items["summary"], "text_1_name": "document", "text_2_name": "summary"}
+                    yield _id, example
+                    _id += 1