# ROUGE Optimization

A text summarization generation model fine-tuned based on t5-small, performing well in the field of financial news

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

T5 Small is the compact version of Google's T5 (Text-to-Text Transfer Transformer) model, suitable for various natural language processing tasks.

This is a summarization model fine-tuned on the CNNDailyMail dataset based on the Flan T5 base model, capable of compressing news articles into concise summaries.

BART Large CNN is a pre-trained model based on the BART architecture, specifically designed for text summarization tasks.

This is an mT5 model fine-tuned for Khmer text summarization tasks, based on Google's mT5-small model. It was fine-tuned on a Khmer text dataset and can generate concise and semantically rich Khmer text summaries.

Fine-tuned based on Google's Pegasus Large model, specifically designed for summarizing lengthy privacy policy documents into concise versions.

A few-shot summarization generation model based on BART-large, trained with 100 samples from the XSUM dataset, demonstrating the potential of few-shot learning in summarization tasks.

PEGASUS Medium is a fine-tuned version of the PEGASUS model, specifically optimized for abstractive text summarization tasks on Indonesian news articles.

A specialized model for scientific paper abstract generation fine-tuned based on the PEGASUS architecture

A text summarization model based on the T5-small architecture, fine-tuned on the multi_news dataset, excelling in generating abstractive summaries

This model is a fine-tuned version of facebook/bart-base for job information summarization, specifically designed to generate compelling summaries from recruitment information.

This model is based on the Pegasus architecture and fine-tuned on the SAMSUM dataset for English dialogue summarization.

distilvit is an image-to-text model based on a VIT image encoder and a distilled GPT-2 text decoder, capable of generating textual descriptions of images.

DistilViT is an image caption generation model based on Vision Transformer (ViT) and distilled GPT-2, capable of converting images into textual descriptions.

An abstractive summarization model fine-tuned on BART, specifically designed for processing lengthy legal documents using a multi-step summarization approach

A summarization model fine-tuned on the cnn_dailymail dataset based on google/mt5-small

A text summarization model fine-tuned based on facebook/bart-base, excelling in generating concise and coherent summaries from lengthy texts.

This model is an Indonesian text summarization model based on the PEGASUS architecture, fine-tuned on the Indosum, Liputan6, and XLSum datasets, suitable for news text summarization tasks.

A Russian text and dialogue summarization model fine-tuned based on ruT5-base, supporting multi-domain Russian text summarization tasks

News summarization model fine-tuned on google/pegasus-xsum, trained on the cnn_dailymail dataset

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

This model is a multilingual summarization model based on the mT5 architecture, specifically fine-tuned for Sinhala to generate abstractive summaries of CNN Daily Mail Sinhala news.

A government report summarization model based on the Long-T5 architecture, specifically optimized for long document summarization tasks

A summary generation model fine-tuned based on facebook/mbart-large-50, specializing in stock event summarization tasks

A fine-tuned T5 model specialized for TLDR news article summarization and headline generation

A news summarization model fine-tuned based on Pegasus architecture, specifically optimized for BBC news content

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

A fine-tuned text summarization model based on barthez-orangesum-title, supporting keyword summarization for French texts

This is an abstract generation model trained based on AutoTrain, specifically designed for biomedical scientific literature.

This is a text summarization model trained via AutoTrain, capable of automatically generating summaries from input text.

A long-text summarization model based on the T5 architecture, capable of processing inputs up to 16,384 tokens, suitable for book and technical document summarization tasks.

A fine-tuned headline generation model based on t5-small, used to generate concise headlines from news text

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 book summarization generation model based on the BART-base architecture and fine-tuned on the BookSum dataset

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