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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+Here is a clean and professional README.md in English, without any emojis.
+
+
+---
+
+#Turkish Wikipedia Topic-to-Summary Dataset
+
+This dataset consists of title–summary pairs extracted from the Turkish Wikipedia XML dump. Each entry contains a topic title as the input and a cleaned, HTML-free summary generated from the first paragraph of the corresponding article as the output.
+It is suitable for training language models, retrieval systems, and knowledge extraction tasks.
+
+#Format
+
+The dataset is provided in JSONL format. Each line represents a single record:
+```
+{
+  "input": "Title",
+  "output": "A short description or summary about the given title."
+}
+```
+Cleaning Process
+
+The following preprocessing steps were applied:
+
+Removal of HTML tags
+
+Removal of HTML entities such as  , &, "
+
+Removal of template structures ({{ ... }})
+
+Removal of Infobox fields and markup
+
+Removal of wiki links ([[Page]], [[Page|Text]])
+
+Normalization of whitespace
+
+Removal of section headers (== Heading ==)
+
+Extraction of the first paragraph or first few sentences
+
+
+All summaries are plain text and designed to be model-friendly.
+
+Possible Use Cases
+
+Training retrieval and ranking models
+
+Topic-to-text or title-to-abstract generation
+
+Knowledge extraction and factual reasoning tasks
+
+Pretraining or finetuning LLMs on structured encyclopedic data
+
+Building question answering systems with short factual outputs
+
+
+Source
+
+The dataset is derived from the publicly available Turkish Wikipedia dump.
+It does not contain any proprietary content and follows Wikipedia's licensing terms.
+
+Size
+
+Format: JSONL
+
+Each record: one topic title and its cleaned summary
+
+Dataset size will depend on the specific Wikipedia dump version used
+
+
+Notes
+
+This dataset is not an official product of Wikipedia or the Wikimedia Foundation. It is a processed derivative created for research and machine learning purposes.
+
+
+---
+
+If you want, I can also prepare:
+
+A shorter version
+
+A GitHub-optimized version
+
+A citation block (BibTeX)
+
+A dataset card in HuggingFace style