update readme (#2)

- update readme (2b2d4137c4c5caca368487fff612aa15c38d64ff)


Co-authored-by: irem <iremddemir@users.noreply.huggingface.co>

README.md

@@ -1,3 +1,109 @@
 ---
+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
+```
+{  
+  
+}
+```
+
+