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GTC ON-DEMAND

Presentation
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Abstract:
We'll describe different approaches to building a summarizer, which creates smaller versions of large documents while retaining the core message. A summarizer can automatically create snippets for the landing pages of web applications or for search engines, and it may reduce the time to read a document by an order of magnitude. We'll discuss the differences between the two approaches to creating a summarizer the statistical approach and the rephrase approach. We'll also examine the algorithm and show a notebook to pre-process the document, set up the machine learning model, and finally deploy this model for real-time invocation.
We'll describe different approaches to building a summarizer, which creates smaller versions of large documents while retaining the core message. A summarizer can automatically create snippets for the landing pages of web applications or for search engines, and it may reduce the time to read a document by an order of magnitude. We'll discuss the differences between the two approaches to creating a summarizer the statistical approach and the rephrase approach. We'll also examine the algorithm and show a notebook to pre-process the document, set up the machine learning model, and finally deploy this model for real-time invocation.  Back
 
Topics:
Deep Learning & AI Frameworks, Speech & Language Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9628
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