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Text Summarize BartBaseCNN Finetune
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Text Summarize BartBaseCNN Finetune
Developed by GilbertKrantz
A text summarization model based on the BART architecture, suitable for the abstract generation task of English scientific papers.
Downloads 0
Release Time : 12/19/2024
Model Overview
This model is based on the BART architecture and is specifically optimized for the abstract generation task of scientific papers. It can extract key information from long scientific papers and generate concise abstracts.
Model Features
Optimization for scientific paper abstracts
Specifically optimized for the characteristics of scientific papers, it can better handle professional terms and complex sentence structures.
Based on the BART architecture
Adopts the BART (Bidirectional Auto-Regressive Transformer) architecture, combining auto-encoding and auto-regressive pre-training objectives.
High-quality training data
Trained using the GilbertKrantz/scientific_papers-cleaned dataset, with high data quality.
Model Capabilities
Text abstract generation
Scientific literature processing
Key information extraction
Use Cases
Academic research
Automatic generation of paper abstracts
Help researchers quickly obtain the core content of papers
Generate concise and accurate paper abstracts
Assistance for literature reviews
Quickly browse the core content of a large number of literatures
Improve the efficiency of literature research
Publishing industry
Journal abstract editing
Assist editors in generating abstracts for submitted papers
Reduce the time for manual abstract writing
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