Imagine you’re a program evaluator or a qualitative researcher tasked with analyzing hundreds of interview transcripts. Each transcript is filled with valuable information, but the sheer volume and time-consuming nature of the task can be overwhelming. You find yourself buried in a sea of words, desperately seeking a way to extract meaningful insights efficiently.  This blog is meant to walk you into a solution by using Azure’s AI Language Service.  

Introduction 

In the realm of program evaluations and qualitative research, interview transcripts hold the key to understanding participants’ perspectives and experiences. However, manually sifting through these transcripts can be an arduous and time-intensive process. This is where Azure AI/ML steps in, offering a game-changing solution that automates the production of summaries and extraction of themes from interview transcripts. We will explore how to leverage Azure AI/ML to support program evaluations and qualitative research. 

The Challenge of Summarization 

Traditionally, summarizing interview transcripts is a time-consuming task, requiring meticulous reading and the ability to identify core themes and statements. This manual process is not only labor-intensive but also prone to inconsistencies. 

Azure AI/ML to the Rescue 

Azure AI/ML services provide a suite of tools designed to tackle the challenge of summarization head-on. With services like Azure AI Language service and Azure OpenAI GPT-3, researchers can automate the summarization process. Additionally, Azure’s extractive summarization API offers a way to pinpoint key sentences that represent the most important information within a transcript.

How It Works 

Summarization: Azure AI Language service can generate summaries of these transcripts. It uses advanced algorithms to identify and condense the most salient points into a coherent summary. 
Theme Extraction: Alongside summarization, Azure AI services can extract key phrases and themes from the text. This is particularly useful for identifying recurring topics or important concepts within a series of interviews. 
Refinement: Researchers can then review and refine these automated summaries and themes, ensuring they align with the research objectives and provide the necessary depth of insight. 

What you need to get started. 

Azure account with a subscription: To create one use the following link: Azure portal Want to know what azure subscription is? azure subscription 
Azure blob storage: A storage account to store documents which need to be extracted. Learn more about azure blob storage: Azure blob storage docs 

Understanding Azure’s AI Language Service 

Azure AI Language Service is a managed cloud service that simplifies the development of natural language processing (NLP) applications. With minimal machine-learning expertise required, it allows users to: 

Identify Key Terms and Phrases: Quickly extract significant words and expressions from text. 
Analyze Sentiment: Determine the emotional tone behind words to understand the context better. 
Summarize Text: Condense long documents into shorter, digestible summaries using both extractive and abstractive techniques. 
Build Conversational Interfaces: Create intelligent chatbots and virtual assistants that can engage with users naturally. 

What is document and conversation summarization? 

Summarization is one of the features offered by Azure AI Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. 

Document summarization only accepts plain text blocks, and conversation summarization accepts various speech artifacts for the model to learn more. 

Key features 

There are the aspects of document summarization this API provides: 

Extractive summarization: Produces a summary by extracting salient sentences within the document. 
Multiple extracted sentences: These sentences collectively convey the main idea of the document. They’re original sentences extracted from the input document’s content. 
Rank score: The rank score indicates how relevant a sentence is to a document’s main topic. Document summarization ranks extracted sentences, and you can determine whether they’re returned in the order they appear, or according to their rank. 
Multiple returned sentences: Determine the maximum number of sentences to be returned. For example, if you request a three-sentence summary extractive summarization returns the three highest scored sentences. 
Positional information: The start position and length of extracted sentences. 
Abstractive summarization: Generates a summary that doesn’t use the same words as in the document but captures the main idea. 
Summary texts: Abstractive summarization returns a summary for each contextual input range within the document. A long document can be segmented so multiple groups of summary texts can be returned with their contextual input range. 
Contextual input range: The range within the input document used to generate the summary text. 

Get started with summarization 

To use summarization, you submit for analysis and handle the API output in your application. Analysis is performed as-is, with no added customization to the model used on your data. 

Input requirements and service limits 

Summarization takes text for analysis. For more information, see Data and service limits in the how-to guide. 
Summarization works with various written languages. For more information, see language support. 

Prerequisites 

An Azure subscription – Create one for free. 

Go to the Azure Portal 

Navigate to Azure Portal and sign-in with credentials that have access or subscription to your resource. 

From the Azure Portal landing page navigate to search button and search language service. 

Let’s Provision Azure AI language Service 

Search for Azure language 

Create Azure AI language service 

Select option 2 to feature to custom summarization and text analytics then click continue to create the resource 

 Under create Azure AI language page 

Choose your subscription. 
Choose or create a resource group. 
Choose the region to deploy (use that is near to your geolocation). 
Create the resource name (make it unique). 
Choose your pricing tier  

Create a storage account or select one if you have. 
Check the box to acknowledge terms in Responsible AI notice 
Then click next. 

As for me I will leave network setting as default and click Review and Create. 

Once deployed navigate to the resource and open it. 

Let’s Navigate to Azure AI Language studio 

Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up language studio quickstart.   

Click the language studio on the service you have deployed successfully. 

Select your preferred language, azure resource that we deployed, number of sentences you prefer in summary and specify your summary interest. 

Upload your .txt file that contains your interview transcript. 
Check the box to acknowledge and Run the model. 

If everything goes well, and all your services are okay, and documents are collected you should get a response like this. 

Congratulations, you have Summarized an interview transcript with Azure AI Language Model.      

The document summarization API request is processed upon receipt of the request by creating a job for the API backend. If the job succeeded, the output of the API is returned. The output is available for retrieval in 24 hours. After this time, the output is purged. Due to multilingual and emoji support, the response might contain text offsets. See how to process offsets for more information. 

For those interested in leveraging these powerful tools to enhance their research process, the web page outlines the necessary steps to get started, including creating an Azure AI language service, setting up Azure blob storage, and navigating the Azure AI Language studio. 

If you’re ready to streamline your research and analysis of interview transcripts, get started with Azure AI/ML today and unlock the potential of automated summarization and theme extraction. 

Read More 

Azure Text Analysis client library for JavaScript 
Azure Text Translation REST client library for JavaScript 
Azure.AI.TextAnalytics Namespace  

Code Samples 

Azure Text Analytics client library samples for JavaScript 
Azure Cognitive Services Text Analytics client library for .NET 
Samples for Azure Text Analytics client library for Python 

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