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Bart Large Summary Map Reduce

Developed by pszemraj
A BART-large-based text summarization model specifically designed for merging chunked summaries of long documents into a final summary
Downloads 43
Release Time : 11/5/2024

Model Overview

This model is a text-to-text generation model primarily used for the 'map-reduce' step in long document summarization tasks, capable of merging multiple chunk-generated summaries into a coherent final summary.

Model Features

Long Document Summarization
Specifically designed for long document summarization tasks, effectively integrating chunk-generated summaries
Efficient Inference
Supports various GPU acceleration methods, including flash-attention2 and torch SDPA
High-Quality Summaries
Fine-tuned based on the BART-large architecture, capable of generating coherent and accurate final summaries

Model Capabilities

Text summarization generation
Long document processing
Summary integration

Use Cases

Document Processing
Long Document Summarization
Integrate multiple summaries generated from chunked processing of long documents into a final summary
Generate a coherent and accurate final summary
Research Report Summarization
Summarize and integrate academic papers or research reports
Extract key information to form concise summaries
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