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BART News Summarizer

Developed by JordiAb
A news summarization model fine-tuned based on BART-large, using StableBeluga-7B as the teacher model to provide efficient and high-quality news summaries
Downloads 44
Release Time : 2/28/2024

Model Overview

This model is specifically designed for generating abstract summaries of news articles, improving inference speed and resource efficiency while maintaining high-quality output

Model Features

Teacher-Student Learning Framework
Uses StableBeluga-7B as the teacher model to guide the training of the BART-large model, balancing quality and efficiency
Efficient Inference
3x faster inference speed compared to the teacher model with significantly reduced GPU memory usage
High-Quality Summaries
ROUGE1 score of 0.66, with a cosine similarity of 0.90 to teacher model summaries
News Domain Optimization
Specifically fine-tuned for news articles, excelling in news summarization tasks

Model Capabilities

News article summarization
English text processing
Efficient inference

Use Cases

News Content Processing
News Aggregation Platform
Automatically generates article summaries for news aggregation platforms
Helps users quickly browse key news points
Media Monitoring
Automatically processes large volumes of news articles and generates summaries
Improves media monitoring efficiency
Content Analysis
Trend Analysis
Quickly analyzes news trends through summaries
Assists in decision-making
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