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Upload vsolscsum.py with huggingface_hub
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vsolscsum.py
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import xml.etree.ElementTree as ET
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from pathlib import Path
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from typing import Dict, List, Tuple
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import datasets
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import pandas as pd
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from seacrowd.utils import schemas
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from seacrowd.utils.configs import SEACrowdConfig
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from seacrowd.utils.constants import Licenses, Tasks
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_CITATION = """\
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@inproceedings{nguyen-etal-2016-vsolscsum,
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title = "{VS}o{LSCS}um: Building a {V}ietnamese Sentence-Comment Dataset for Social Context Summarization",
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author = "Nguyen, Minh-Tien and
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Lai, Dac Viet and
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Do, Phong-Khac and
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Tran, Duc-Vu and
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Nguyen, Minh-Le",
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editor = "Hasida, Koiti and
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Wong, Kam-Fai and
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Calzorari, Nicoletta and
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Choi, Key-Sun",
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booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
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month = dec,
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year = "2016",
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address = "Osaka, Japan",
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publisher = "The COLING 2016 Organizing Committee",
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url = "https://aclanthology.org/W16-5405",
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pages = "38--48",
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}
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"""
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_DATASETNAME = "vsolscsum"
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_DESCRIPTION = """
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The Vietnamese dataset for social context summarization \
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The dataset contains 141 open-domain articles along with \
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3,760 sentences, 2,448 extracted standard sentences and \
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comments as standard summaries and 6,926 comments in 12 \
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events. This dataset was manually annotated by human. \
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Note that the extracted standard summaries also include comments.\
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The label of a sentence or comment was generated based on the \
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voting among social annotators. For example, given a sentence, \
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each annotator makes a binary decision in order to indicate \
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that whether this sentence is a summary candidate (YES) or not \
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(NO). If three annotators agree yes, this sentences is labeled by 3. \
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Therefore, the label of each sentence or comment ranges from 1 to 5\
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(1: very poor, 2: poor, 3: fair, 4: good; 5: perfect). The standard \
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summary sentences are those which receive at least three agreements \
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from annotators. The inter-agreement calculated by Cohen's Kappa \
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after validation among annotators is 0.685.
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"""
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_HOMEPAGE = "https://github.com/nguyenlab/VSoLSCSum-Dataset"
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_LANGUAGES = ["vie"]
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_LICENSE = Licenses.CC_BY_4_0.value
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_LOCAL = False
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_URLS = {
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_DATASETNAME: "https://raw.githubusercontent.com/nguyenlab/VSoLSCSum-Dataset/master/VSoSLCSum.xml",
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}
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_SUPPORTED_TASKS = [Tasks.SUMMARIZATION]
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_SOURCE_VERSION = "1.0.0"
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_SEACROWD_VERSION = "2024.06.20"
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class VSolSCSumDataset(datasets.GeneratorBasedBuilder):
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"""
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The Vietnamese dataset for social context summarization includes 141 articles
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with a total of 3,760 sentences. It also contains 2,448 standard sentences
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extracted along with comments serving as standard summaries, and 6,926 c
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omments across 12 events. Human annotators manually curated this dataset.
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Each sentence or comment received a label from 1 to 5 based on annotators'
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agreement (1: very poor, 2: poor, 3: fair, 4: good, 5: perfect). Standard
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summary sentences are those with at least three agreements. The inter-agreement
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among annotators, measured by Cohen's Kappa, is 0.685.
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"""
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
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BUILDER_CONFIGS = [
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SEACrowdConfig(
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name=f"{_DATASETNAME}_source",
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version=SOURCE_VERSION,
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description=f"{_DATASETNAME} source schema",
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schema="source",
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subset_id=f"{_DATASETNAME}",
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),
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SEACrowdConfig(
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name=f"{_DATASETNAME}_seacrowd_t2t",
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version=SEACROWD_VERSION,
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description=f"{_DATASETNAME} SEACrowd schema",
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schema="seacrowd_t2t",
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subset_id=f"{_DATASETNAME}",
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),
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]
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
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def _info(self) -> datasets.DatasetInfo:
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if self.config.schema == "source":
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features = datasets.Features(
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{
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"post_id": datasets.Value("string"),
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"title": datasets.Value("string"),
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"summary": datasets.Value("string"),
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"document_and_comment": datasets.Value("string"),
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}
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)
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elif self.config.schema == "seacrowd_t2t":
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features = schemas.text2text_features
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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+
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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"""Returns SplitGenerators."""
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data_path = Path(dl_manager.download_and_extract(_URLS[_DATASETNAME]))
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": data_path,
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"split": "train",
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},
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)
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]
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+
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
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"""Yields examples as (key, example) tuples."""
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with open(filepath, "r", encoding="utf-8") as file:
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xml_content = file.read()
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root = ET.fromstring(xml_content)
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def extract_data_from_xml(root):
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data = []
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+
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for post in root.findall(".//post"):
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post_id = post.get("id")
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title = post.find("title").text
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summary_sentences = [sentence.find("content").text for sentence in post.find(".//summary").find("sentences").findall("sentence")]
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document_sentences = [sentence.find("content").text for sentence in post.find(".//document").find("sentences").findall("sentence")]
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comment_sentences = [sentence.find("content").text for sentence in post.find(".//comments").find(".//comment").find("sentences").findall("sentence")]
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+
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summary_text = " ".join(summary_sentences)
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document_text = " ".join(document_sentences)
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comment_text = " ".join(comment_sentences)
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+
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data.append(
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{
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"post_id": post_id,
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"title": title,
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"summary": summary_text,
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"document_and_comment": f"{document_text} | {comment_text}",
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}
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)
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+
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return data
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+
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extracted_data = extract_data_from_xml(root)
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df = pd.DataFrame(extracted_data)
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+
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for index, row in df.iterrows():
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+
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if self.config.schema == "source":
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example = row.to_dict()
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+
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elif self.config.schema == "seacrowd_t2t":
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+
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example = {
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"id": str(row["post_id"]),
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"text_1": str(row["summary"]),
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"text_2": str(row["document_and_comment"]),
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"text_1_name": "summary",
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"text_2_name": "document_and_comment",
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}
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yield index, example
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