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Bert Roberta Summarization Cnn Dailymail

Developed by Ayham
A text summarization model fine-tuned on the CNN/DailyMail dataset based on BERT or RoBERTa architecture
Downloads 57
Release Time : 3/2/2022

Model Overview

This model is a text summarization model specifically designed for news article summarization tasks. It is based on the Transformer architecture (BERT or RoBERTa variants) and fine-tuned on the CNN/DailyMail dataset.

Model Features

News Summarization Optimization
Summarization capabilities specifically optimized for news article content
Transformer-based Architecture
Utilizes BERT or RoBERTa architecture with strong contextual understanding capabilities
Large-scale Dataset Training
Fine-tuned on the CNN/DailyMail dataset, containing a large number of news article samples

Model Capabilities

News article summarization
Long text compression
Key information extraction

Use Cases

News Media
Automatic News Summarization
Automatically generates concise summaries for lengthy news articles
Improves reader efficiency by quickly capturing key news points
Content Aggregation
News Digest Creation
Automatically generates summary content for daily news digests
Reduces manual editing workload
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