import os from pathlib import Path from typing import Dict, List, Tuple import datasets from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Tasks from seacrowd.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" _SEACROWD_VERSION = "2024.06.20" 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) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = ( [ SEACrowdConfig( 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) ] + [ SEACrowdConfig( name="indosum_fold{fold_number}_seacrowd_t2t".format(fold_number=i), version=_SEACROWD_VERSION, description="indosum Nusantara schema", schema="seacrowd_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 == "seacrowd_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 == "seacrowd_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