Hugging Face is a popular open-source library for NLP, with over 49,000 pre-trained models in more than 185 languages with support for different frameworks. You can apply this NLP technique to longer-form text documents and articles, enabling quicker consumption and more effective document indexing, for example to summarize call notes from meetings. Text summarization is a helpful technique in understanding large amounts of text data because it creates a subset of contextually meaningful information from source documents. It can perform text analysis on a wide variety of languages for its various features.
For example, Amazon Comprehend can perform NLP tasks such as custom entity recognition, sentiment analysis, key phrase extraction, topic modeling, and more to gather insights from text. AWS offers pre-trained AWS AI services that can be integrated into applications using API calls and require no ML experience. To better understand this growing data, machine learning (ML) natural language processing (NLP) techniques for text analysis have evolved to address use cases involving text summarization, entity recognition, classification, translation, and more.
Global data volumes are growing at zettabyte scale as companies and consumers expand their use of digital products and online services. Based on the steps shown in this post, you can try summarizing text from the WikiText-2 dataset managed by fast.ai, available at the Registry of Open Data on AWS.
#Auto summarize tool medical how to#
In this post, we show you how to implement one of the most downloaded Hugging Face pre-trained models used for text summarization, DistilBART-CNN-12-6, within a Jupyter notebook using Amazon SageMaker and the SageMaker Hugging Face Inference Toolkit.
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