How many of them are there? If you've got 100's of them, you could do it for a couple bucks or less with gpt-3 davinci without a ton of prompt engineering. If I had a ton of them I'd probably go with gpt-j and see if could do a serviceable job with few-shot learning.

No I need scalability, so consider millions.

If you have the budget, gpt3/gpt3.5 are pretty great at it.

If you want to do it on your hardware/cloud instances.

You can go for gpt-j or flan-T5, they do great with basic prompts.

Or you can try a finetuned model, you might be able to get a finetuned T5 or there's BART and pegasus as well.

For summarisation even smaller models are pretty good so don't just jump to the biggest models. Compare the performance.

Fine-tuning flan T5 xl or XXL can give you decent results. From my experience these are best open source models to fine-tune on . However they won't match results of larger models like gpt 3.5. But if you have millions of such reviews then chatgpt or gpt 3.5 may not be financially feasible.

Since you mentioned Huddingface, What do you think of Pegasus for Abstractive Summarization?

Flan models are trained in almost every open dataset available in Generic English tasks. Recent research suggests models trained to perform multiple tasks (in fact ratios of different tasks too affect see flan 2022 paper) are better than models trained only on a given task. Flan T5 beats T5 in almost every task and sometimes Flan T5 XXL matches gpt3 type of prompt generation.

Many thanks for your input!

Flan t5 xl is a very good transformer finetuned on instructions. T5 is also good. You might want to tweak around some generation parameters to get good results.