# How does summarization work with pretrained models? **URL:** \ **Category:** 🤗Transformers\ **Created:** [November 14, 2023, 2:54pm UTC](https://discuss.huggingface.co/t/how-does-summarization-work-with-pretrained-models/62220 "2023-11-14T14:54:49Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![gkzsolt](https://avatars.discourse-cdn.com/v4/letter/g/ad7895/32.png) [@gkzsolt](https://discuss.huggingface.co/u/gkzsolt)\ **Post date:** [November 14, 2023, 2:54pm UTC](https://discuss.huggingface.co/t/how-does-summarization-work-with-pretrained-models/62220/1 "2023-11-14T14:54:49Z")
Hi all, I am getting to know HuggingFace pipelines and trying to find a model for summarizing reviews. I tried the following models: `sshleifer/distilbart-xsum-12-1`, `t5-base`, `ainize/bart-base-cnn`, `gavin124/gpt2-finetuned-cnn-summarization-v2` and `google/pegasus-xsum`. My experience is below: **sshleifer/distilbart-xsum-12-1** ```auto summarizer = pipeline("summarization", model='sshleifer/distilbart-xsum-12-1') # 443M summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20) [{'summary_text': ' A doctor in the UK has been diagnosed with cancer.'}] summarizer(""" Your max_length is set to 20, but your input_length is only 11. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=5)""") [{'summary_text': ' If you want to know what it is a good idea for you.'}] ``` **t5-base** ```auto summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf") # 892M summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20) [{'summary_text': 'an apple a day, keeps the doctor away from the doctor .'}] summarizer("""summarize: This was a really good movie, although the second part was quite boring, me and my wife almost fall asleep. But the beginning was crazy! So it was not bad to watch.""", min_length=5, max_length=15) [{'summary_text': 'the second part of this movie was quite boring . the movie was'}] ``` **ainize/bart-base-cnn** ```auto summarizer = pipeline("summarization", model="ainize/bart-base-cnn") # 558M summarizer("""This was a really good movie, although the second part was quite boring, me and my wife almost fall asleep. But the beginning was crazy! So it was not bad to watch.""", min_length=5, max_length=15) [{'summary_text': '"This was a really good movie, although the second part'}] ``` **gavin124/gpt2-finetuned-cnn-summarization-v2** ```auto summarizer = pipeline("summarization", model="gavin124/gpt2-finetuned-cnn-summarization-v2", framework="pt") summarizer("This was a really good movie, although the second part was quite boring, me and my wife almost fall asleep. But the beginning was crazy! So it was not bad to watch.") [{'summary_text': 'This was a really good movie, although the second part was quite boring, me and my wife almost fall asleep. But the beginning was crazy! So it was not bad to watch. I'}] ``` **google/pegasus-xsum** ```auto summarizer = pipeline("summarization", model="google/pegasus-xsum", framework="pt") # 2.28G summarizer("This was a pretty bad movie: the second part was quite boring, me and my wife almost fall asleep. But the beginning was crazy! ") [{'summary_text': 'This is the second part of a two-part review of the horror film The Conjuring.'}] ``` **GPT-3.5** ```auto summarize: An apple a day, keeps the doctor away. 'The saying "an apple a day, keeps the doctor away" emphasizes the idea that consuming an apple every day can help maintain good health and prevent the need for medical attention.' summarize in 15 tokens: """This was a really good movie, although the second part was quite boring, me and my wife almost fall asleep. But the beginning was crazy! So it was not bad to watch.""" 'Good movie, boring part almost slept, crazy beginning, not bad.' ``` These make the impression that the open source models cannot really summarize. Is that the state of the art or I am missing something? Should I do something, like extra train the (pretrained) model with some other corpus to be able to summarize? Thank you,