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vsolscsum.py

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+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