Mt5 Small Finetuned Arxiv Cs Finetuned Arxiv Cs Full
This model is a text summarization model fine-tuned on the arXiv computer science paper dataset based on mt5-small, excelling at generating concise summaries of technical content.
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Release Time : 3/2/2022
Model Overview
This is a small multilingual Transformer model based on the mT5 architecture, specifically fine-tuned for academic paper summarization tasks in the computer science domain.
Model Features
Academic Paper Optimization
Specifically optimized for arXiv papers in the computer science domain, effectively handling technical terms and complex concepts
Efficient Summarization
Generates concise summaries while retaining key information, achieving a ROUGE-1 score of 39.89
Lightweight Model
Based on the mT5-small architecture, maintaining good performance with a compact model size
Model Capabilities
Technical document summarization
Academic paper content condensation
Multilingual text processing
Use Cases
Academic Research
Rapid Paper Reading
Helps researchers quickly grasp the core content of computer science papers
ROUGE-1 score of 39.89 indicates effective capture of key information from the original text
Knowledge Management
Literature Database Summarization
Automatically generates standardized abstracts for academic literature databases
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