# Long Text Summarization

A meeting transcript summarization model optimized based on the BigBird-Pegasus architecture, capable of processing meeting records up to 4096 tokens and generating concise summaries

A text summarization model based on the Mistral 7B architecture, optimized for efficiency and performance using LoRA technology.

A text summarization model fine-tuned based on pegasus-x-base, trained with synthetic data, excelling in long document summarization tasks.

A specialized summarization model fine-tuned for 2 rounds based on Qwen2-1.5B-Instruct

A text summarization model fine-tuned based on the BART architecture, specifically designed for generating concise summaries of conversations and news articles.

A fine-tuned text summarization model based on the BART architecture, designed to generate concise and accurate summaries

A text summarization model based on the Long-T5 architecture, fine-tuned using the synthetic dataset synthsumm, specializing in long text summarization tasks.

An open-source long-context language model fine-tuned based on Meta's original Llama-2 7B model, supporting 32K context length

A summarization model based on the Long-T5 architecture, supporting multilingual text summarization tasks.

An English long-text summarization model fine-tuned based on Pegasus-x-large, supporting abstractive summarization of complex texts such as academic manuscripts and meeting minutes

A text summarization model trained on the AutoTrain platform, utilizing the BART-Longformer architecture

A LongT5-XL model fine-tuned on the BookSum dataset, specifically designed for long-text summarization, capable of generating SparkNotes-like summaries.

This model is a fine-tuned version of led-base-16384 on the billsum dataset, specifically designed for long document summarization tasks.

A large-scale summarization model based on the Long-T5 architecture, specifically optimized for book and long document summarization tasks

An optimized long-text summarization model based on the T5 architecture, capable of processing inputs up to 16,384 tokens, excelling in book summarization tasks.

A large-scale summarization model based on the Long-T5 architecture, specifically optimized for long-document summarization tasks, supporting a context length of 16,384 tokens.

A BART model based on LSG technology, optimized for long-sequence summarization tasks, supporting input sequences up to 16,384 tokens in length

LongT5 is a long-sequence text-to-text Transformer model based on T5, employing transient-global attention mechanism, suitable for long-text processing tasks.

BART-base model based on LSG technology, fine-tuned for long text summarization tasks on the MediaSum dataset, supporting sequence processing up to 4096 tokens

A BART-base model based on LSG technology, designed for long-text summarization tasks, supporting input sequences of up to 4096 tokens

Long text summarization model based on LSG-BART architecture, fine-tuned on the WCEP-10 dataset, supports processing long sequences of up to 4096 tokens

A long-sequence text summarization model based on the BART architecture, specifically fine-tuned for the PubMed scientific paper dataset, capable of processing input sequences up to 16,384 tokens in length

A long-sequence processing model based on LSG attention mechanism, fine-tuned specifically for scientific paper summarization tasks

A long-sequence processing model based on the BART architecture, optimized for scientific paper summarization tasks, supporting long-text input up to 16,384 tokens

The LSG model is an improved long-sequence processing model based on BART-large, utilizing local + sparse + global attention mechanisms for efficient handling of long-text tasks

A large-scale summarization model based on the PEGASUS architecture, fine-tuned on the booksum dataset, excels at generating easy-to-understand SparkNotes-style summaries

BigBird is a Transformer model based on sparse attention, capable of processing sequences up to 4096 in length, suitable for tasks like long document summarization.

BigBird is a Transformer model based on sparse attention, capable of handling longer sequences, suitable for tasks like long document summarization.

BigBirdPegasus is a Transformer model based on sparse attention, capable of handling longer sequences, especially suitable for long document summarization tasks.

Pegasus is a Transformer-based sequence-to-sequence model specifically designed for text summarization tasks. This model is based on Google's Pegasus architecture and fine-tuned to generate high-quality summaries.