Learn More About Natural Language Generation (NLG) Software
What is NLG software?
Natural language generation software uses artificial intelligence technology to generate natural written or spoken language text that people can understand. It analyzes structured and unstructured data sets to transform that information into natural-sounding human language.
For instance, NLG software can turn financial data into an earnings report or sports data into a match summary. It can also interpret user intent by analyzing customer input and generating personalized responses, eliminating the need for manual intervention. This functionality enables voice assistants and chatbots to respond with a human touch.
What are the common features of natural language generation software?
NLG software solutions have robust features and components for converting data into contextual content.
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Text generation: The primary function of NLG software is to produce natural language text from structured data and other knowledge sources. The generated text comes in many forms, like reports, summaries, emails, and presentations.
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Contextual understanding: NLG software tools can understand the user intent behind a command or query and provide a contextual response in a human-like language. This ability improves system and user communication by providing personalized answers.
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Enhancing data visualization. When integrated with visualization tools, natural language generation software can create reports, including textual summaries and visual representations. It writes descriptive narratives to accompany visuals, like charts and graphs, making complex data accessible to a broader audience.
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Automated document creation. With NLG technology, users can create documents without any manual effort. The software takes input in the form of structured data and creates a draft based on the user’s chosen template.
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Language variation: This feature allows NLG software to generate text in different styles and languages, which is especially useful for businesses communicating with different groups.
Types of NLG software
NLG software is broadly classified into two types: standalone solutions and built-in tools. Both serve different purposes.
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Standalone NLG: Standalone solutions operate independently; they come with their own user interfaces instead of being tied to a specific platform. More flexible than their counterparts, they can be used for various content generation needs like creating reports, writing articles, and composing product descriptions.
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Built-in NLG: These are integrated into larger software platforms like analytics or business intelligence. They provide context-specific text generation within the environment of the existing application, enhancing its overall functionality.
Key benefits of natural language generation software
NLG software improves data handling and communication for organizations, both internally and externally. More of its advantages are described here.
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Easy-to-digest data: NLG software translates complex data into natural language, making it easy for individuals without specialized data analysis skills to understand. As such, users grasp data insights more easily.
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Plain language interface: NLG tools generate content in human-like language, which is simple, natural, and free from specialized terminology. This helps businesses reach a wider audience.
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Content creation assistance: NLG automates writing tasks by processing structured data and producing contextually relevant content. It’s a powerful tool for scaling up the content creation workflow, and it also helps generate reports, product descriptions, summaries, emails, and other content forms.
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Less manual effort: Since NLG automates the content creation process, it reduces the need for human intervention. It frees up human resources and allows your team to focus on more strategic and creative tasks without hampering the volume of consistent content flow.
Natural language generation vs. natural language processing vs. natural language understanding
NLP, NLG, and NLU are all closely related concepts under the umbrella of AI. They work together to produce effective communication between machines and humans. However, each differs slightly in functionality, i.e., how computers handle human language.
NLG is all about generating natural language text from complex data sets, whereas NLU focuses specifically on understanding natural language. For instance, NLU is used in voice assistants like Siri or Google Assistant to respond to commands and queries by understanding user intent.
NLP is the broader field encompassing both generation and understanding of natural language. It facilitates communication between the user and the system by enabling the tech to understand, interpret, and respond to human language. Common use cases of NLP include speech recognition and machine translation.
Who uses natural language generation software?
Anyone looking to transform structured data into intelligible textual content can use NLG software. It's especially valuable for industries that require consistent data-driven content creation or personalized communications with their stakeholders.
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Analysts: Business, financial, and data analysts use NLG software to generate reports and summaries. NLG makes data more accessible and understandable, so it’s easy to discover trends and communicate numbers with all stakeholders.
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Content creators: NLG systems are useful for churning out large volumes of high-quality content, which makes it an essential tool for today’s creators. They can use NLG’s automation tactics for writing social media copy, blog posts, and product descriptions while maintaining a consistent brand message.
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Department heads: NLG software enables department heads to improve efficiency and communication within their teams and beyond. They use it for tracking and sharing metrics and facilitating data-driven decisions across the organization.
NLG software pricing
The price for natural language generation software depends greatly on the project scope, features, and vendor. Factors like customization, training and implementation, maintenance, and storage also affect the overall cost of the product.
Depending on the needs and financial preferences of the user, there are mainly two kinds of pricing models for NLG software.
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Add-on: Some platforms offer core services and allow customers to upgrade by purchasing NLG features as add-ons. This model is structured on a monthly fee plus the base subscription cost. It enables a customized user experience that only includes features as needed.
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Included: In other cases, some platforms incorporate NLG as a part of their standard offerings and charge no additional fee. Under this pricing model, the base cost is high, but NLG features are a part of the subscription fee. It also simplifies billing and can work for extensive users.
If you don’t need NLG that often, then purchasing it as an add-on is more cost-effective and flexible. It’s usually billed on a monthly fee subscription model. On the other hand, if NLG is essential for daily operations, a standard subscription included with the platform will be more beneficial.
ROI for NLG software
You can assess your return on investment for NLG software through factors that contribute to the overall value and benefits derived from its implementation.
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Ease of use: Since NLG software is easy to use, more people can apply it without needing training.
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Time saved: The automation of content creation saves significant time and cost because fewer human resources are required to generate data-driven reports and other documents.
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Scalability: With NLG software, organizations scale up their content production without requiring additional resources. This results in higher production output, but costs remain the same.
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Productivity: Without the need to constantly write and create, employees can focus on tasks that drive value and lead to profitable business outcomes.
What are the alternatives to NLG software?
These alternatives to NLG software can replace it, at least partially, if not fully.
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Analytics platforms collect, analyze, and visualize data from different resources. They provide features like data visualization, reporting, and statistical analysis to uncover patterns, trends, and insights. Many analytics platforms also offer a basic level of narrative explanation alongside data visualizations for accessible communication.
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AI chatbots are automated messaging systems that conduct interactive human conversations. They interpret user inputs, process requests, and respond in natural language. AI, machine learning, and NLP technology are all used to understand questions and generate relevant replies.
Which companies should buy natural language generation software?
NLG software is especially useful for companies that manage large volumes of data and need coherent narrative insights from it.
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E-commerce and retail organizations: These companies can use NLG solutions to generate large volumes of content, like product descriptions, reviews, and sales reports. These tools can also process customer data to provide tailored shopping experiences and write content for different consumer groups.
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Financial institutions: NLG software helps banks and financial institutes by creating automated financial reports and investment summaries. It also makes it easier to communicate financial insights to clients and stakeholders across the board.
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Healthcare: By using NLG software, healthcare providers can interpret complex medical and clinical data and generate reports, patient summaries, and other content based on electronic health records.
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Marketing and advertising agencies: NLG software produces data-driven and creative ad copies, email campaigns, and social media content. Its performance reports help you tap into data insights.
How to choose natural language generation software
Choosing the right natural language software requires the following steps.
Identify business needs and priorities
Stakeholders must start by evaluating the areas within their organization where NLG can add value, whether it's automating report generation, enhancing content creation, or improving customer interaction. Understanding business goals and testing the technical proficiency of the staff is crucial.
Choose the necessary technology and features
The next step is finding solutions that carry the technology and features aligned with your needs. NLG software with comprehensive language models produces coherent and accurate text. Customization and flexibility are essential to achieve the desired tone and style of content. Data integration helps connect existing databases and resources. Businesses with regional and worldwide operations appreciate the multilingual service.
Review vendor vision, roadmap, viability, and support
Once features are identified, stakeholders must explore and narrow their vendor choices. Examining product roadmap, vendor's business mission, viability, and customer support are good starting points. Market stability and user reviews are also essential factors for long-term planning.
Evaluate the deployment and pricing model
When considering software deployment, businesses have to weigh the benefits of cloud-based versus on-site solutions versus a hybrid model. Evaluate factors like flexibility, control, compliance, and security, along with the licensing fees and subscription costs. The pricing model should be transparent and scalable.
Put it all together
Finally, stakeholders must take all considerations and create an actionable plan. This step involves identifying potential risks, taking feedback from end users, and conducting a final evaluation to choose the best solution that aligns with business objectives, priorities, and budget.
Natural language generation software trends
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The rise of NLP and conversational AI: NLP advancements have improved contextual understanding, interpretation, and generation of human language, allowing NLG software to generate more relevant and coherent text. This is particularly vital in conversational AI used in chatbots and virtual assistants for understanding user intent and maintaining context.
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User-friendly conversational interface: The success of platforms like ChatGPT highlights the importance of user-friendly chat platforms. It increased the adoption and integration of NLG technology into diverse applications. The interface allows users to communicate with the system using natural language, even if unfamiliar with complex commands and interfaces.
