Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Entity Based Abstractive Text Summarizer
By:
Latest Version:
3.7
A solution to generate entity focused abstractive summary for text documents.
Product Overview
This solution helps users automate the coherent summary generation from documents. It identifies frequently used entity clusters in the document to capture salient and most important candidate sentences. An abstractive summary is generated from these sentences using reinforcement learning to enhance coherence, conciseness, and clarity.
Key Data
Version
By
Type
Model Package
Highlights
This solution uses deep learning to identify salient sentences using entity clusters. Policy based reinforcement learning is used to enhance the quality of content captured by the summarizer and improve the readability of the summary.
This solution can be applied across industries such as education, pharmaceutical, finance, HR for document management, legal contract analysis, search engine optimization, paraphrasing, and media monitoring among others.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$10.00/hr
running on ml.m5.2xlarge
Model Batch Transform$20.00/hr
running on ml.m5.2xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.461/host/hr
running on ml.m5.2xlarge
SageMaker Batch Transform$0.461/host/hr
running on ml.m5.2xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.m4.4xlarge | $10.00 | |
ml.m5.4xlarge | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge Vendor Recommended | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.p2.16xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.m5.xlarge | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.p3.8xlarge | $10.00 | |
ml.m5.large | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Amazon SageMaker
Input
- Supported content type:
text/plain. - The input file has to be in utf-8 encoding only
- For best performance, please limit the number of sentences to 60 with an average word limit of 20 per sentence.
- The algorithm can take a maximum of 5000 characters.
Output
- Content type:
text/plain. - The output renders the summary in a text file.
Invoking endpoint
AWS CLI Command If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://Input.txt --content-type text/plain --accept application/zip output.txtSubstitute the following parameters:
"endpoint-name"- name of the inference endpoint where the model is deployed.Input.txt- Input file.text/plain- MIME type of the given input file.output.txt- filename where the inference results are written to.
Resources
Additional Resources
End User License Agreement
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Support Information
Entity Based Abstractive Text Summarizer
For any assistance reach out to us at:
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