Add loading script

lr-sum.py

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+"""LR-Sum summarization dataset"""
+
+import json
+import os
+
+import datasets
+
+_CITATION = """\
+@inproceedings{palen-michel-lignos-2023-lr,
+    title = "{LR}-Sum: Summarization for Less-Resourced Languages",
+    author = "Palen-Michel, Chester  and
+      Lignos, Constantine",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.findings-acl.427",
+    doi = "10.18653/v1/2023.findings-acl.427",
+    pages = "6829--6844",
+    abstract = "We introduce LR-Sum, a new permissively-licensed dataset created with the goal of enabling further research in automatic summarization for less-resourced languages.LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. We describe our process for extracting and filtering the dataset from the Multilingual Open Text corpus (Palen-Michel et al., 2022).The source data is public domain newswire collected from from Voice of America websites, and LR-Sum is released under a Creative Commons license (CC BY 4.0), making it one of the most openly-licensed multilingual summarization datasets. We describe abstractive and extractive summarization experiments to establish baselines and discuss the limitations of this dataset.",
+}
+"""
+
+_DESCRIPTION = """\
+We introduce LR-Sum, a new permissively-licensed dataset created with the goal of enabling further research in automatic summarization for less-resourced languages. 
+LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. 
+We describe our process for extracting and filtering the dataset from the Multilingual Open Text corpus (Palen-Michel et al., 2022).
+The source data is public domain newswire collected from from Voice of America websites, and LR-Sum is released under a Creative Commons license (CC BY 4.0), making it one of the most openly-licensed multilingual summarization datasets. 
+We describe abstractive and extractive summarization experiments to establish baselines and discuss the limitations of this dataset.
+"""
+
+_HOMEPAGE = "https://github.com/bltlab"
+
+_LICENSE = "Creative Commons Attribution 4.0 International (CC-BY 4.0)"
+
+_URL = "https://huggingface.co/datasets/bltlab/lr-sum/resolve/main/data/{}.zip"
+
+_LANGUAGES = [
+    "amh",
+    "aze",
+    "ben",
+    "bod",
+    "bos",
+    "ckb",
+    "cmn_t",
+    "cmn_s",
+    "ell",
+    "eng",
+    "fas",
+    "fra",
+    "hat",
+    "hau",
+    "hye",
+    "ind",
+    "kat",
+    "khm",
+    "kin",
+    "kor",
+    "kmr",
+    "lao",
+    "mkd",
+    "mya",
+    "nde",
+    "por",
+    "prs",
+    "pus",
+    "rus",
+    "sna",
+    "som",
+    "spa",
+    "sqi",
+    "srp",
+    "swh",
+    "tha",
+    "tir",
+    "tur",
+    "ukr",
+    "urd",
+    "uzb",
+    "vie",
+]
+
+
+class Lrsum(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="{}".format(lang),
+            version=datasets.Version("1.0.0")
+        )
+        for lang in _LANGUAGES
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "summary": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+            version=self.VERSION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        lang = str(self.config.name)
+        url = _URL.format(lang)
+
+        data_dir = dl_manager.download_and_extract(url)
+        ret = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, lang + "_test.jsonl"),
+                },
+            )
+        ]
+        if os.path.exists(os.path.join(data_dir, lang + "_train.jsonl")):
+            ret.append(datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, lang + "_train.jsonl"),
+                },
+            )
+            )
+        if os.path.exists(os.path.join(data_dir, lang + "_val.jsonl")):
+            ret.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, lang + "_val.jsonl"),
+                    },
+                )
+            )
+
+        return ret
+
+    def _generate_examples(self, filepath):
+        """Yields examples as (key, example) tuples."""
+        with open(filepath, encoding="utf-8") as f:
+            for idx_, row in enumerate(f):
+                data = json.loads(row)
+                yield idx_, {
+                    "id": data["id"],
+                    "url": data["url"],
+                    "title": data["title"],
+                    "summary": data["summary"],
+                    "text": data["text"],
+                }