# Text Summarization

A text summarization model trained based on the mt5-base architecture, capable of extracting key information from text to generate summaries.

A text summarization model fine-tuned on the XSum dataset based on T5-small

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

A model specialized in text summarization tasks, capable of extracting key information from long texts to generate concise summaries

This model is a summarization generation model capable of automatically generating concise summaries of input text.

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

A text summarization model fine-tuned based on the GPT-2 architecture, supporting PyTorch and CoreML frameworks, suitable for generating concise and accurate text summaries.

SummLlama3.2-3B is a 3.2B-parameter summary generation model optimized based on the Llama3 architecture, offering multiple quantization versions to accommodate different hardware requirements.

This is an ONNX-optimized version of the facebook/bart-large-cnn model, primarily used for text summarization tasks.

A text processing model fine-tuned based on the T5-small architecture, focusing on text generation and transformation for specific tasks

A fine-tuned model based on T5-small architecture, primarily used for text generation tasks, with average performance on ROUGE metrics

A dialogue extraction model fine-tuned based on qwen-1.8, capable of batch extracting summaries and dialogues from novel excerpts

DistilBART-CNN-12-6 is a distilled version of the BART model, optimized for text summarization tasks, with a smaller size while maintaining high performance.

A text summarization model based on FLAN-T5, suitable for automatic summarization of English texts.

A large-scale text summarization model based on the BART architecture, optimized for the CNN/DailyMail dataset

OpenVINO IR format version of the T5-small model, supporting text generation, translation, and other tasks

DistilBART is a distilled version of the BART model, optimized for text summarization tasks, significantly improving inference speed while maintaining high performance.

PEGASUS is an abstractive summarization model based on pre-training with gap sentences, developed by Google Research.

A text processing model fine-tuned based on the distilroberta-base model, suitable for general NLP tasks

A text summarization generation model fine-tuned on the distilbart-cnn-12-6 model, trained for 1000 steps with weakly supervised data

BiMeanVAE is a model based on Variational Autoencoder (VAE), primarily used for text summarization tasks.

KeyBART is a pre-trained text generation model based on the BART architecture, specifically designed to generate concatenated keyphrase sequences in the CatSeqD format.

A weakly supervised topic label generation model based on BART, solving topic labeling tasks through generation rather than selection

DistilBART is a distilled version of the BART model, optimized for text summarization tasks, significantly reducing model size and inference time while maintaining high performance.

KoGPT2 is a Korean GPT-2 model developed by SKT-AI, based on the Transformer architecture, suitable for various Korean text generation tasks.

A text summarization model fine-tuned on an unknown dataset based on T5-small, excelling at generating concise summaries