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Welcome to the Hugging Face tasks series.  

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In this video, we will take a look 
at the Text Summarization task. 

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Summarization is a task of 
producing a shorter version  

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of a document while preserving the relevant 
and important information in the document. 

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Summarization models take a document to be 
summarized and output the summarized text. 

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This task is evaluated on the ROUGE score. It’s 
based on the overlap between the produced sequence  

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and the correct sequence.
You might see this as ROUGE-1,  

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which is the overlap of single tokens and ROUGE-2, 
the overlap of subsequent token pairs. ROUGE-N  

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refers to the overlap of n subsequent tokens. 
Here we see an example of how overlaps take place. 

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An example dataset used for this task is 
called Extreme Summarization, XSUM. This  

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dataset contains texts and 
their summarized versions. 

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You can use summarization models 
to summarize research papers which  

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would enable researchers to easily 
pick papers for their reading list. 

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For more information about the Summarization 
task, check out the Hugging Face course.
