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indosum.py

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+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+from nusacrowd.utils import schemas
+import jsonlines
+from nltk.tokenize.treebank import TreebankWordDetokenizer
+
+_CITATION = """\
+@INPROCEEDINGS{8629109,
+  author={Kurniawan, Kemal and Louvan, Samuel},
+  booktitle={2018 International Conference on Asian Language Processing (IALP)}, 
+  title={Indosum: A New Benchmark Dataset for Indonesian Text Summarization}, 
+  year={2018},
+  volume={},
+  number={},
+  pages={215-220},
+  doi={10.1109/IALP.2018.8629109}}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_DATASETNAME = "indosum"
+
+_DESCRIPTION = """\
+INDOSUM is a new benchmark dataset for Indonesian text summarization. 
+The dataset consists of news articles and manually constructed summaries.
+"""
+
+_HOMEPAGE = "https://github.com/kata-ai/indosum"
+
+_LICENSE = "Apache License, Version 2.0"
+
+_URLS = {
+    _DATASETNAME: "https://drive.google.com/uc?id=1OgYbPfXFAv3TbwP1Qcwt_CC9cVWSJaco",
+}
+
+_SUPPORTED_TASKS = [Tasks.SUMMARIZATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class IndoSUM(datasets.GeneratorBasedBuilder):
+    """INDOSUM is a new benchmark dataset for Indonesian text summarization. The dataset consists of news articles and manually constructed summaries."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = (
+        [
+            NusantaraConfig(
+                name="indosum_fold{fold_number}_source".format(fold_number=i),
+                version=_SOURCE_VERSION,
+                description="indosum source schema",
+                schema="source",
+                subset_id="indosum_fold{fold_number}".format(fold_number=i),
+            ) for i in range(5)
+        ]
+        +
+        [
+            NusantaraConfig(
+            name="indosum_fold{fold_number}_nusantara_t2t".format(fold_number=i),
+            version=_NUSANTARA_VERSION,
+            description="indosum Nusantara schema",
+            schema="nusantara_t2t",
+            subset_id="indosum_fold{fold_number}".format(fold_number=i),
+        ) for i in range(5)
+        ]
+    )
+
+    DEFAULT_CONFIG_NAME = "indosum_fold0_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+
+            features = datasets.Features(
+               {
+                   "document": datasets.Value("string"),
+                   "id": datasets.Value("string"),
+                   "summary": datasets.Value("string")
+               }
+            )
+
+        elif self.config.schema == "nusantara_t2t":
+            features = schemas.text2text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _get_fold_index(self):
+        try:
+            subset_id = self.config.subset_id
+            idx_fold = subset_id.index("_fold")
+            file_id = subset_id[(idx_fold + 5):]
+            return int(file_id)
+        except:
+            return 0
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        idx = self._get_fold_index()
+
+        urls = _URLS[_DATASETNAME]
+
+        data_dir = Path(dl_manager.download_and_extract(urls))
+
+        location = {
+            "train": "indosum/train.0{fold_number}.jsonl",
+            "test": "indosum/test.0{fold_number}.jsonl",
+            "dev": "indosum/dev.0{fold_number}.jsonl"
+        }
+
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, location["train"].format(fold_number=idx+1)),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, location["test"].format(fold_number=idx+1)),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, location["dev"].format(fold_number=idx+1)),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _get_full_paragraph_and_summary(self, data: Dict) -> Tuple[str, str]:
+        detokenizer = TreebankWordDetokenizer()
+        paragraph = ""
+        summary = ""
+        begin_paragraph = True
+        begin_summary = True
+
+        for each_paragraph in data["paragraphs"]:
+            for each_sentence in each_paragraph:
+                detokenized_sentence = detokenizer.detokenize(each_sentence)
+                if begin_paragraph:
+                    paragraph+=detokenized_sentence
+                    begin_paragraph = False
+                else:
+                    paragraph = "{} {}".format(paragraph, detokenized_sentence)
+        
+        for each_summary in data["summary"]:
+            detokenized_sentence = detokenizer.detokenize(each_summary)
+            if begin_summary:
+                summary+=detokenized_sentence
+                begin_summary = False
+            else:
+                summary = "{} {}".format(summary, detokenized_sentence)
+
+        return paragraph, summary
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+
+        if self.config.schema == "source":
+            i = 0
+            with jsonlines.open(filepath) as f:
+                for each_data in f.iter():
+                    full_paragraph, full_summary = self._get_full_paragraph_and_summary(each_data)
+                    ex = {
+                        "id": each_data["id"],
+                        "document": full_paragraph,
+                        "summary": full_summary
+                    }
+                    yield i, ex
+                    i+=1
+
+        elif self.config.schema == "nusantara_t2t":
+            i = 0
+            with jsonlines.open(filepath) as f:
+                for each_data in f.iter():
+                    full_paragraph, full_summary = self._get_full_paragraph_and_summary(each_data)
+                    ex = {
+                        "id": each_data["id"],
+                        "text_1": full_paragraph,
+                        "text_2": full_summary,
+                        "text_1_name": "document",
+                        "text_2_name": "summary"
+                    }
+                    yield i, ex
+                    i+=1