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Bert2bert Cnn Daily Mail
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Bert2bert Cnn Daily Mail
Developed by patrickvonplaten
A summarization model based on the BERT2BERT architecture, fine-tuned specifically for the CNN/DailyMail dataset, suitable for news article summarization tasks.
Downloads 1,072
Release Time : 3/2/2022
Model Overview
This model is an encoder-decoder model based on the BERT2BERT architecture, fine-tuned specifically for the CNN/DailyMail news summarization dataset, capable of generating high-quality news article summaries.
Model Features
High-Quality Summarization
Achieves a ROUGE-2 score of 18.6853 on the CNN/DailyMail test set, capable of generating coherent and accurate news summaries.
Warm-Started from BERT
Utilizes pre-trained BERT models as a foundation, improving training efficiency and model performance through warm-starting.
Encoder-Decoder Architecture
Adopts a Transformer-based encoder-decoder structure, ideal for sequence-to-sequence text generation tasks.
Model Capabilities
News Article Summarization
Long Text Compression
Key Information Extraction
Use Cases
News Media
Automatic News Article Summarization
Generates concise and accurate summaries for lengthy news articles
Achieves a ROUGE-L score of 28.191 on the CNN/DailyMail test set
Content Aggregation
Multi-Source News Summarization
Generates unified summaries for news content from various sources
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