xtinge/turkish-extractive-summarization-dataset

---
dataset_info:
- config_name: mlsum_tr_ext
- config_name: xtinge-sum_tr_ext
- config_name: tes
configs:
- config_name: mlsum_tr_ext
data_files:
- split: train
path: MLSUM_TR_EXT/train*
- split: test
path: MLSUM_TR_EXT/test*
- split: val
path: MLSUM_TR_EXT/val*
- config_name: xtinge-sum_tr_ext
data_files:
- split: test
path: XTINGE-SUM_TR_EXT/XTINGE-SUM_TR_EXT*
- config_name: tes
data_files:
- split: test
path: TES/tes*
task_categories:
- summarization
license: gpl-3.0
---
# XTINGE Turkish Extractive Summarization Datasets

This repository hosts three datasets created for advancing Turkish extractive text summarization research: MLSUM_TR_EXT, TES, and XTINGE-SUM_TR_EXT. These datasets are designed to support the development of models capable of generating concise and relevant extractive summaries of Turkish texts.

Below is a Python example showcasing how to download and use these datasets:

```python
from datasets import load_dataset

# Load the MLSUM_TR_EXT dataset
mlsum_tr_ext = load_dataset("xtinge/turkish-extractive-summarization-dataset", "mlsum_tr_ext")
# Load the TES dataset
tes = load_dataset("xtinge/turkish-extractive-summarization-dataset", "tes")
# Load the xtinge-sum_tr_ext dataset
xtinge_sum_tr_ext = load_dataset("xtinge/turkish-extractive-summarization-dataset", "xtinge-sum_tr_ext")

```

## Dataset Details

### Dataset Description

The datasets, having a focus on Turkish text summarization, aim to advance research in this area by providing structured, annotated resources for extractive summarization tasks. These datasets are:

1. **MLSUM_TR_EXT**:
- Originates as an extension of the Turkish subset from the [MLSUM dataset](https://huggingface.co/datasets/mlsum), focusing on extractive summarization.
- Comprises articles from internethaber.com, with summaries derived from existing headlines for creating contextually rich extractive summaries.
- Sentences within these articles were selected based on their SBERT Similarity and ROUGE Scores compared to the original summaries, ensuring relevance and conciseness.

2. **TES**:
- Represents a unique collection found on [Hugging Face](https://huggingface.co/erturkerdagi/turkishExtractiveSummarization/tree/main) tailored for Turkish extractive summarization.
- Contains a variety of news articles annotated by three distinct annotators, each providing different perspectives and lengths, thus contributing to a rich set of summarization examples.

3. **XTINGE-SUM_TR_EXT**:
- Specifically developed to supplement existing resources by providing detailed sentence importance rankings within lengthy Wikipedia documents.
- Features annotations by three different annotators who meticulously ranked all sentences by importance, contributing to a comprehensive resource for studying extractive summarization.
- The annotation process considered Inter Annotator Agreement, specifically employing Krippendorff's alpha to ensure consistency and reliability in sentence importance assessments.

- **Language(s) (NLP):** Turkish
- **License:** [gpl-3.0]

## Dataset Structure

### Generic Structure Across Datasets

All three datasets share a generic structure tailored for extractive summarization tasks, comprising the following elements:

- **Title**: The title of the document or article, serving as a concise representation of the content.
- **Sentences**: The body of the text, split into sentences. This segmentation facilitates the identification of individual sentences that contribute to the summary.
- **Annotations**: This section includes annotations for selecting summary sentences. It is subdivided into:
- **Indexes**: Indices of sentences that have been selected for the summary. This field varies across datasets based on the number of annotators.
- **Ranking**: Rankings assigned to sentences based on their perceived importance for the summary. This feature is more prominent in datasets focusing on sentence importance ranking.

```python
{
'Title': '<title_of_document>',
'Sentences': ['<sentence_1>', '<sentence_2>', ..., '<sentence_n>'],
'Annotations': {
'Indexes': {

'Annotator1': [<index_of_selected_sentence_1>, ..., <index_of_selected_sentence_m>],
# If there are more than one annotator
'Annotator2': [...],
# etc.
},
'Ranking': {
'Annotator1': [<ranking_of_first_sentence>,<ranking_of_second_sentence>,..., <ranking_of_mth_sentence>],
# If there are more than one annotator
'Annotator2': [...],
# etc.
}
}
}
```

## Cite XTINGE Turkish Extractive Summarization Dataset
```
@inproceedings{xtinge_turkish_extractive,
title = {Extractive Summarization Data Sets Generated with Measurable Analyses},
author = {Demir, İrem and Küpçü, Emel and Küpçü, Alptekin},
booktitle = {Proceedings of the 32nd IEEE Conference on Signal Processing and Communications Applications},
year = {2024}
}
```

数据集信息：
- 配置名称：mlsum_tr_ext
- 配置名称：xtinge-sum_tr_ext
- 配置名称：tes
数据集配置：
- 配置名称：mlsum_tr_ext
数据文件：
- 拆分集：训练集（train），路径：MLSUM_TR_EXT/train*
- 拆分集：测试集（test），路径：MLSUM_TR_EXT/test*
- 拆分集：验证集（val），路径：MLSUM_TR_EXT/val*
- 配置名称：xtinge-sum_tr_ext
数据文件：
- 拆分集：测试集（test），路径：XTINGE-SUM_TR_EXT/XTINGE-SUM_TR_EXT*
- 配置名称：tes
数据文件：
- 拆分集：测试集（test），路径：TES/tes*
任务类别：
- 摘要任务（summarization）
许可证：gpl-3.0
---
# XTINGE土耳其语抽取式摘要（extractive summarization）数据集

本仓库收录了为推进土耳其语抽取式文本摘要研究而构建的三类数据集：MLSUM_TR_EXT、TES与XTINGE-SUM_TR_EXT。这些数据集旨在支持开发能够生成简洁且贴合主题的土耳其语文本抽取式摘要的模型。

以下是一段展示如何下载并使用这些数据集的Python示例代码：
python
from datasets import load_dataset

# 加载MLSUM_TR_EXT数据集
mlsum_tr_ext = load_dataset("xtinge/turkish-extractive-summarization-dataset", "mlsum_tr_ext")
# 加载TES数据集
tes = load_dataset("xtinge/turkish-extractive-summarization-dataset", "tes")
# 加载xtinge-sum_tr_ext数据集
xtinge_sum_tr_ext = load_dataset("xtinge/turkish-extractive-summarization-dataset", "xtinge-sum_tr_ext")

## 数据集详情

### 数据集概述
本系列数据集聚焦土耳其语文本摘要任务，通过提供结构化、带标注的抽取式摘要研究资源，推动该领域的研究进展。三类数据集详情如下：

1. **MLSUM_TR_EXT**：
- 源自[MLSUM数据集](https://huggingface.co/datasets/mlsum)土耳其子集的扩展版本，专注于抽取式摘要任务。
- 数据集包含来自internethaber.com的新闻文章，其摘要基于现有标题生成，以构建上下文丰富的抽取式摘要。
- 文章内的句子通过与原始摘要的SBERT相似度（SBERT Similarity）与ROUGE得分（ROUGE Score）进行筛选，以确保摘要内容的相关性与简洁性。

2. **TES**：
- 是收录于[Hugging Face平台](https://huggingface.co/erturkerdagi/turkishExtractiveSummarization/tree/main)的专属土耳其语抽取式摘要数据集。
- 包含各类新闻文章，由三名不同标注员完成标注，每位标注员提供的摘要角度与长度各不相同，丰富了摘要样本的多样性。

3. **XTINGE-SUM_TR_EXT**：
- 专为补充现有研究资源而开发，针对长维基百科文档提供详细的句子重要性排序标注。
- 数据集由三名不同标注员对所有句子按重要性进行逐一排序，为抽取式摘要研究提供了全面的标注资源。
- 标注过程中考虑了标注者间一致性，采用Krippendorff's alpha系数来确保句子重要性评估的一致性与可靠性。

- **自然语言处理语言**：土耳其语
- **许可证**：[gpl-3.0]

## 数据集结构

### 数据集通用结构
三类数据集均采用适配抽取式摘要任务的通用结构，包含以下核心元素：
- **标题**：文档或文章的标题，用于简洁概括内容主题。
- **句子**：拆分后的文本正文，按句子进行分段，便于识别可纳入摘要的独立句子。
- **标注信息**：包含用于筛选摘要句子的标注内容，细分为：
- **索引**：被选中作为摘要的句子索引，该字段因标注者数量不同而存在差异。
- **排序**：基于句子对摘要任务的重要性赋予的评分，该特性在侧重句子重要性排序的数据集中更为突出。

python
{
'Title': '<文档标题>',
'Sentences': ['<句子1>', '<句子2>', ..., '<句子n>'],
'Annotations': {
'Indexes': {
'标注员1': [<选中句子的索引1>, ..., <选中句子的索引m>],
# 若存在多名标注员
'标注员2': [...],
# 其他标注员
},
'Ranking': {
'标注员1': [<第1句的重要性评分>,<第2句的重要性评分>,..., <第m句的重要性评分>],
# 若存在多名标注员
'标注员2': [...],
# 其他标注员
}
}
}

## 引用XTINGE土耳其语抽取式摘要数据集

@inproceedings{xtinge_turkish_extractive,
title = "基于可量化分析生成的抽取式摘要数据集",
author = {Demir, İrem and Küpçü, Emel and Küpçü, Alptekin},
booktitle = {第32届IEEE信号处理与通信应用会议论文集},
year = {2024}
}

原始信息汇总

XTINGE Turkish Extractive Summarization Datasets

数据集概述

该数据集包含三个用于土耳其文本抽取式摘要研究的子数据集：MLSUM_TR_EXT、TES 和 XTINGE-SUM_TR_EXT。这些数据集旨在支持开发能够生成简洁且相关摘要的模型。

数据集详情

数据集描述

1. MLSUM_TR_EXT:

   • 源自 MLSUM 数据集 的土耳其子集扩展，专注于抽取式摘要。
   • 包含来自 internethaber.com 的文章，摘要由现有标题生成，以创建上下文丰富的抽取式摘要。
   • 文章中的句子根据与原始摘要的 SBERT 相似度和 ROUGE 分数进行选择，确保相关性和简洁性。

2. TES:

   • 代表一个独特的集合，可在 Hugging Face 上找到，专为土耳其抽取式摘要设计。
   • 包含由三位不同标注者标注的各种新闻文章，每个标注者提供不同的视角和长度，从而提供丰富的摘要示例。

3. XTINGE-SUM_TR_EXT:

   • 专门开发以补充现有资源，提供详细的句子重要性排序，适用于长篇维基百科文档。
   • 由三位不同的标注者仔细对所有句子按重要性进行排序，为研究抽取式摘要提供全面的资源。
   • 标注过程中考虑了标注者间的一致性，特别是使用 Krippendorffs alpha 来确保句子重要性评估的一致性和可靠性。

数据集结构

所有三个数据集共享一个适用于抽取式摘要任务的通用结构，包括以下元素：

• Title: 文档或文章的标题，作为内容的简洁表示。
• Sentences: 文本的主体，分割成句子。这种分割有助于识别对摘要有贡献的单个句子。
• Annotations: 包括选择摘要句子的标注。分为：
  • Indexes: 被选为摘要的句子索引。该字段因标注者数量而异。
  • Ranking: 根据句子对摘要的重要性分配的排名。这一特征在专注于句子重要性排序的数据集中更为突出。

python { Title: <title_of_document>, Sentences: [<sentence_1>, <sentence_2>, ..., <sentence_n>], Annotations: { Indexes: { Annotator1: [<index_of_selected_sentence_1>, ..., <index_of_selected_sentence_m>], # 如果有多个标注者 Annotator2: [...], # 等等 }, Ranking: { Annotator1: [<ranking_of_first_sentence>,<ranking_of_second_sentence>,..., <ranking_of_mth_sentence>], # 如果有多个标注者 Annotator2: [...], # 等等 } } }

引用

@inproceedings{xtinge_turkish_extractive, title = {Extractive Summarization Data Sets Generated with Measurable Analyses}, author = {Demir, İrem and Küpçü, Emel and Küpçü, Alptekin}, booktitle = {Proceedings of the 32nd IEEE Conference on Signal Processing and Communications Applications}, year = {2024} }