| import xml.etree.ElementTree as ET | |
| from pathlib import Path | |
| from typing import Dict, List, Tuple | |
| import datasets | |
| import pandas as pd | |
| from seacrowd.utils import schemas | |
| from seacrowd.utils.configs import SEACrowdConfig | |
| from seacrowd.utils.constants import Licenses, Tasks | |
| _CITATION = """\ | |
| @inproceedings{nguyen-etal-2016-vsolscsum, | |
| title = "{VS}o{LSCS}um: Building a {V}ietnamese Sentence-Comment Dataset for Social Context Summarization", | |
| author = "Nguyen, Minh-Tien and | |
| Lai, Dac Viet and | |
| Do, Phong-Khac and | |
| Tran, Duc-Vu and | |
| Nguyen, Minh-Le", | |
| editor = "Hasida, Koiti and | |
| Wong, Kam-Fai and | |
| Calzorari, Nicoletta and | |
| Choi, Key-Sun", | |
| booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)", | |
| month = dec, | |
| year = "2016", | |
| address = "Osaka, Japan", | |
| publisher = "The COLING 2016 Organizing Committee", | |
| url = "https://aclanthology.org/W16-5405", | |
| pages = "38--48", | |
| } | |
| """ | |
| _DATASETNAME = "vsolscsum" | |
| _DESCRIPTION = """ | |
| The Vietnamese dataset for social context summarization \ | |
| The dataset contains 141 open-domain articles along with \ | |
| 3,760 sentences, 2,448 extracted standard sentences and \ | |
| comments as standard summaries and 6,926 comments in 12 \ | |
| events. This dataset was manually annotated by human. \ | |
| Note that the extracted standard summaries also include comments.\ | |
| The label of a sentence or comment was generated based on the \ | |
| voting among social annotators. For example, given a sentence, \ | |
| each annotator makes a binary decision in order to indicate \ | |
| that whether this sentence is a summary candidate (YES) or not \ | |
| (NO). If three annotators agree yes, this sentences is labeled by 3. \ | |
| Therefore, the label of each sentence or comment ranges from 1 to 5\ | |
| (1: very poor, 2: poor, 3: fair, 4: good; 5: perfect). The standard \ | |
| summary sentences are those which receive at least three agreements \ | |
| from annotators. The inter-agreement calculated by Cohen's Kappa \ | |
| after validation among annotators is 0.685. | |
| """ | |
| _HOMEPAGE = "https://github.com/nguyenlab/VSoLSCSum-Dataset" | |
| _LANGUAGES = ["vie"] | |
| _LICENSE = Licenses.CC_BY_4_0.value | |
| _LOCAL = False | |
| _URLS = { | |
| _DATASETNAME: "https://raw.githubusercontent.com/nguyenlab/VSoLSCSum-Dataset/master/VSoSLCSum.xml", | |
| } | |
| _SUPPORTED_TASKS = [Tasks.SUMMARIZATION] | |
| _SOURCE_VERSION = "1.0.0" | |
| _SEACROWD_VERSION = "2024.06.20" | |
| class VSolSCSumDataset(datasets.GeneratorBasedBuilder): | |
| """ | |
| The Vietnamese dataset for social context summarization includes 141 articles | |
| with a total of 3,760 sentences. It also contains 2,448 standard sentences | |
| extracted along with comments serving as standard summaries, and 6,926 c | |
| omments across 12 events. Human annotators manually curated this dataset. | |
| Each sentence or comment received a label from 1 to 5 based on annotators' | |
| agreement (1: very poor, 2: poor, 3: fair, 4: good, 5: perfect). Standard | |
| summary sentences are those with at least three agreements. The inter-agreement | |
| among annotators, measured by Cohen's Kappa, is 0.685. | |
| """ | |
| SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) | |
| SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) | |
| BUILDER_CONFIGS = [ | |
| SEACrowdConfig( | |
| name=f"{_DATASETNAME}_source", | |
| version=SOURCE_VERSION, | |
| description=f"{_DATASETNAME} source schema", | |
| schema="source", | |
| subset_id=f"{_DATASETNAME}", | |
| ), | |
| SEACrowdConfig( | |
| name=f"{_DATASETNAME}_seacrowd_t2t", | |
| version=SEACROWD_VERSION, | |
| description=f"{_DATASETNAME} SEACrowd schema", | |
| schema="seacrowd_t2t", | |
| subset_id=f"{_DATASETNAME}", | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" | |
| def _info(self) -> datasets.DatasetInfo: | |
| if self.config.schema == "source": | |
| features = datasets.Features( | |
| { | |
| "post_id": datasets.Value("string"), | |
| "title": datasets.Value("string"), | |
| "summary": datasets.Value("string"), | |
| "document_and_comment": 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.""" | |
| data_path = Path(dl_manager.download_and_extract(_URLS[_DATASETNAME])) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": data_path, | |
| "split": "train", | |
| }, | |
| ) | |
| ] | |
| def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: | |
| """Yields examples as (key, example) tuples.""" | |
| with open(filepath, "r", encoding="utf-8") as file: | |
| xml_content = file.read() | |
| root = ET.fromstring(xml_content) | |
| def extract_data_from_xml(root): | |
| data = [] | |
| for post in root.findall(".//post"): | |
| post_id = post.get("id") | |
| title = post.find("title").text | |
| summary_sentences = [sentence.find("content").text for sentence in post.find(".//summary").find("sentences").findall("sentence")] | |
| document_sentences = [sentence.find("content").text for sentence in post.find(".//document").find("sentences").findall("sentence")] | |
| comment_sentences = [sentence.find("content").text for sentence in post.find(".//comments").find(".//comment").find("sentences").findall("sentence")] | |
| summary_text = " ".join(summary_sentences) | |
| document_text = " ".join(document_sentences) | |
| comment_text = " ".join(comment_sentences) | |
| data.append( | |
| { | |
| "post_id": post_id, | |
| "title": title, | |
| "summary": summary_text, | |
| "document_and_comment": f"{document_text} | {comment_text}", | |
| } | |
| ) | |
| return data | |
| extracted_data = extract_data_from_xml(root) | |
| df = pd.DataFrame(extracted_data) | |
| for index, row in df.iterrows(): | |
| if self.config.schema == "source": | |
| example = row.to_dict() | |
| elif self.config.schema == "seacrowd_t2t": | |
| example = { | |
| "id": str(row["post_id"]), | |
| "text_1": str(row["summary"]), | |
| "text_2": str(row["document_and_comment"]), | |
| "text_1_name": "summary", | |
| "text_2_name": "document_and_comment", | |
| } | |
| yield index, example | |