# English Summarization

A text summarization model fine-tuned on the xsum dataset based on the T5-small architecture, capable of generating concise summaries.

This model is a text summarization model fine-tuned on the samsum dataset based on google/pegasus-cnn_dailymail, specifically designed for dialogue 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.

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

A T5-small fine-tuned model for generating summaries of U.S. Congressional bills

A news summarization model fine-tuned based on BART-large, using StableBeluga-7B as the teacher model to provide efficient and high-quality news 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 automatic summarization model trained based on FLAN-T5 large, suitable for English text summarization tasks.

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

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

A summarization model fine-tuned on the xsum dataset based on facebook/bart-base, excelling in generating concise and accurate summaries.

This is a sequence-to-sequence model based on the BART architecture, specifically designed for dialogue summarization tasks.

An English text summarization generator fine-tuned on the BART model, trained on the CNN-DailyMail dataset

A text summarization model based on GPT2 architecture, specifically optimized for CNN/Daily Mail dataset

This model is a fine-tuned news summarization model based on Google's T5-base architecture, trained on a dataset containing 4,515 news summarization samples

An encoder-decoder model initialized with RoBERTa-Large, specifically designed for summarization tasks and fine-tuned on the CNN/DailyMail dataset.