Hi,
I can suggest starting looking here:
  

      paperswithcode.com
  

  
    

Papers with Code - XSum Dataset 13

  The Extreme Summarization (XSum) dataset is a dataset for evaluation of abstractive single-document summarization systems. The goal is to create a short, one-sentence new summary answering the question “What is the article about?”. The dataset...


  

  
    
    
  

  


I haven’t found neither to be honest. But!
As I believe it seems smarter to use encoder-decoder style models (like PEGASUS or BART) for summarization. Decoder-only language models like GPT were not only trained to continue texts (unlike ~BART), but also they don’t extract the idea of the given texts. Though encoder can be interpreted as an “idea extractor” and the decoder as the generator for natural language text.
P.S. I know one paper that tries to prove the point on summarizing using GPT3 is better than using BART. Even though it heavily relies on Russian language in experiments - you can use the references in the paper to look deeper and find what you are looking for. Paper arxiv link:
  
      

      arXiv.org
  

  
    

Fine-tuning GPT-3 for Russian Text Summarization 12

  Automatic summarization techniques aim to shorten and generalize information
given in the text while preserving its core message and the most relevant
ideas. This task can be approached and treated with a variety of methods,
however, not many...


  

  
    
    
  

  


Good luck and let me know if you find anything,
Kirill