Multi Document Summarization Made Easier Using teX-Ai

There is much excitement around data today because of the potential it holds in unlocking new opportunities for businesses. Progressive organizations are using digital transformation and data analytics as key tools to drive strategic decision-making.

Take the retail industry for example. Data analytics powers almost everything from operational efficiency, product lifecycle management, and inventory management to customer retention strategies, social media approaches, and competitor assessment.

There is, of course, no dearth of data. Sticking with our example of the retail industry – there is data from in-store analytics, e-commerce sites, social media chatter, customer service calls, reviews on online shopping sites, competitor analysis research reports, and even inputs from subject matter experts within and outside of the company.

The International Data Corporation (IDC) believes that we will be generating 180 zeta bytes by 2025 from 4.4 zeta bytes in 2013. That’s like an avalanche! We can be buried in it with no clue of how to use this data to our advantage.

On one end is numeric data, which can be processed using data models and statistical tools. But real insights are often derived from qualitative information as well. This information is hidden inside of trend reports, consumer reviews, annual reports of competitors, or even in audio and video recordings.

Do we ignore these inputs because they are difficult to process? Absolutely not. Enter the world of text analytics, where relevant and needed information is extracted from text documents (pdf files, slides, research reports), images, audio and video files, to name a few.

An automated approach for Text Summarization solution

What needed is text summarization, where elaborate documents need to be shortened and only the key points made available to the relevant employees to enable them to leverage the insights into something more meaningful. The image and A/V inputs also need to be extracted and integrated to provide a holistic view.

However, no individual or even large pools of resources can achieve this given the volume of data that needs to be gathered and perused. This is where machine learning and NLP analytics solution comes to the rescue.

But the elimination of the human element from the summarization process raises yet another challenge. Document summarization tools need to have an understanding of semantic concepts that reflects user’s interests and task requirements rather than merely a simplistic correlative translation approach.

You might also want to read: The Semantic Search Capabilities of teX.ai: Why It Is a Key Differentiator

In other words, an effective test summarization process clearly knows the intent of the process.

One tool that has proved to be very effective in using deep learning for text extraction tool and automatic text summarization is teX.ai. For automation of insights generation, teX.ai uses machine learning models to train and understand documents and extract information from a variety of sources. The data thus extracted is then summarized using text summarization rules to produce documents that require shorter reading time, faster understanding and research, and quicker decision-making.

teX.ai – For Improved Text Analytics Solutions

teX.ai is an AI-powered text analytics software that follows a three-pronged approach:

• Text Extraction
• Text Summarization
• Text Classification

Document Summarization is extremely relevant from several different sectors. In this blog, we highlight use cases from BFSI and Retail to healthcare and manufacturing.

• BFSI & FinTech: The world of financial services is going through a transformation. FinTech companies, banks, NBFCs, and insurance companies are relying heavily on data analytics to drive growth, increase operational efficiency, spot opportunities to cross-sell and up-sell, launch better products and services, etc. Automated Document Summarization from KYC documents, customer history, trend reports, consumer reviews has now become a core part of any data science or data analytics process. Banks are also using document summarization to analyse bank statements and loan documents, with the key goal of reducing the time taken from analysis to insight. In the insurance industry, document summarization is being used for faster processing of claims, party-automated underwriting, and even deriving insights to launch pocket-size insurance products.

• In Retail and eCommerce, document summarization is being used to drive several key decision-making processes. On the customer side, documents put together from consumer reviews and complaints are put to work in tandem with sales data. This helps a retailer understand its consumers better and make decisions to drive growth and enhance brand loyalty. The point is a robust and efficient multi document summarization process makes it easy to include qualitative data into the data analytics process. For consumer-facing businesses, it is becoming increasingly important to not only derive strategic decisions based on numeric data like sales numbers and pricing but also through sentiment analysis solution. The process of sentiment analysis becomes way more efficient with a powerful document summarization engine.

• In the Manufacturing industry, as a leader we can extract text from documents such as invoices, supplier documents, audit reports, inputs from plant heads, delivery receipts, and engineering drawings for further enhancing internal processes across the organization.

• In the healthcare sector too, we can see service providers benefiting from an automated document summarization process, one that can efficiently and effectively put together summaries from key clinical processes, hospital experience for patients, and even their experience of interfacing with various insurance providers.

We have served enterprise clients in retail, e-commerce, banking, financial services, fintech, insurance, manufacturing, and healthcare. Over the last few years, we’ve realized the significant role of qualitative data from documents and how insights from this data can make a major difference to our clients

This drives us in the right direction to build teX.ai, a world-class AI-based Text Analytics Software for Text Extraction, Summarization & Classification.

If you’d like to understand more about how teX.ai can help your enterprise, schedule a text summarization demo.