Create README.md

README.md

@@ -0,0 +1,67 @@
+---
+task_categories:
+- summarization
+language:
+- en
+---
+
+
+
+## Dataset Card for processed_dataset_top.csv
+
+This dataset is an enhanced version of the CNN/DailyMail summarization dataset. Articles have been preprocessed and keywords are prepended at the top of each article to provide additional context for fine-tuning summarization models.
+## Dataset Details
+# Dataset Description
+
+The dataset includes news articles with keywords prepended at the top, formatted with special tokens for compatibility with transformer-based models. Keywords were extracted using KeyBERT to emphasize key topics from the articles. Each article is paired with its corresponding summary (highlights).
+Dataset Sources
+Original Dataset
+
+The original dataset is the CNN/DailyMail summarization dataset, which contains:
+
+    Articles: News articles from CNN and DailyMail.
+    Highlights: Human-written summaries of the articles.
+
+## Preprocessing Applied
+# Keyword Extraction:
+
+    Extracted keywords using KeyBERT.
+    Keywords were formatted with <keyword> special tokens and prepended at the top of each article.
+
+# Dataset Structure
+
+The dataset contains two columns:
+
+    article: Preprocessed articles with keywords prepended at the top.
+    highlights: Preprocessed summaries (highlights).
+
+# Example:
+Article:
+
+Keywords: <keyword>GLOBAL ECONOMY</keyword>, <keyword>INFLATION</keyword>, <keyword>SUPPLY CHAIN</keyword>
+
+The global economy is facing unprecedented challenges due to inflation and supply chain disruptions.
+
+Highlights:
+
+Global economy faces challenges from inflation and supply chain issues.
+
+## Intended Use
+
+This dataset was created to provide an enhanced summarization dataset for experiments in keyword-based summarization. Prepending keywords at the top of the text acts as a primer, potentially improving model performance by focusing attention on key topics early.
+# Possible Use Cases:
+
+    Fine-tuning summarization models such as DistilBART or BART.
+    Evaluating the impact of prepended contextual keywords on summarization accuracy.
+
+## Limitations
+
+    Contextual Bias:
+        Prepending keywords may introduce a bias where the model overly focuses on the prepended keywords rather than the article's main content.
+
+    Keyword Extraction Quality:
+        Automatically extracted keywords might not always reflect the true focus of the article.
+
+## Citation
+
+If using this dataset, please cite the original CNN/DailyMail summarization dataset and mention the preprocessing and keyword extraction enhancements.