In today's digital age, the capacity to analyse large amounts of textual data quickly and properly is critical. Extracting relevant information from text can be difficult, whether you're analysing customer comments or summarising lengthy documents. However, advances in natural language processing (NLP) and the availability of powerful tools such as Azure Cognitive Services have made the process substantially more accessible. In this post, we'll look at how to use Azure Cognitive Services for Language in Python to build summary documents that condense complex content into brief and informative excerpts.

Understanding Azure Cognitive Services for Language

Azure Cognitive Services is a set of cloud-based APIs and services that allow developers to integrate AI capabilities into their applications without requiring considerable machine learning experience. The Language service in this suite provides a variety of features, such as sentiment analysis, text analytics, entity recognition, and key phrase extraction. By leveraging these capabilities, we can extract critical information from text and develop summaries that reflect the spirit of the original content.

Setting up Azure Language resource

Go to Azure Portal and search Language, then click on "Create".

Click on "Continue" to create your resource.

Choose the subscription, resource group, region, pricing tier, type the resource name, and check the box that acknowledges the terms of Responsible AI. Then, click on "Review + Create".

Once the resource is created, go to "Keys and Endpoint" to copy your credentials.

Getting Started with Azure Language on Python

You must install the Azure AI Text Analytics SDK and Pdfplumber (to extract text from PDF files). To do this, run the following statements in your Python environment:

Next, import the required libraries and authenticate with your Azure account.

We must pass only text to the text_analytics_client; thus, we must extract the text from the documents ourselves.

The PDF file used in this example is a magazine and has 9 pages.

We must extract the text from each page and save it in a list that we will send to text_analytics_client.

Let us start with the summary extraction.

Once we have a summary per page, we must utilise a loop to print it.

The following is the outcome:

Conclustion

Azure Cognitive Services for Language offers a wide range of capabilities to efficiently process textual data. By utilising its functionalities in Python, we may exploit the potential of natural language processing to produce concise papers that encapsulate the core of intricate content. Azure Cognitive Services enables developers to streamline information extraction and enhance their applications with robust natural language processing (NLP) capabilities, ranging from key phrase extraction to sentence ranking. Explore the realm of Azure Cognitive Services and unleash the ability to convert text into valuable knowledge.