Natural Language Generation Software Trends
Natural language generation (NLG) software converts labeled data into human language, allowing you to automatically generate reports, summaries, and other informative content from your data without the need for time-consuming writing and data analysis.
NLG software often works in tandem with natural language processing (NLP), though the two still have plenty of individual uses. Here, where NLP converts natural language into data, NLG does the opposite, instead converting data into natural language. Working together, NLG and NLP often form the backbone of many chatbots and similar human-interfacing AI.
On its own, NLG software is largely used for generating summaries and reports. Though it may be a convenient tool, NLG software is quickly becoming a necessity as datasets grow larger. With humans unable to analyze and report large amounts of data as efficiently, NLG software will likely become a necessity for any data-based business or application.
Why use NLG software?
NLG software allows you to instantly generate reports from large datasets, saving you the time and trouble of having to analyze and describe the data yourself.
NLG software does the writing for you
Writing can be tedious and time-consuming, especially when it comes to writing about numbers. Even those who love to write find the process difficult when it comes to interpreting and communicating data.
Here, unlike fiction or other creative writing, technical writing requires more precision and practice than most people are willing to invest. Plus, accuracy and clarity in technical reports can make or break projects; if certain figures are misrepresented or miscommunicated, those dependent on the reports might not be able to make effective decisions.
With so much on the line in technical reports, many companies look for ways to outsource the writing. While technical writers are often the preferred solution, it may be difficult to find technical writers specialized to particular industries. As a result, many organizations are left to doing the work themselves— only to take time and budgets away from more important work.
Thankfully, NLG software offers an ideal middle ground. While software may not be robust enough to replace a specialized technical writer, it’s more than capable of automatically stringing data together into valid, accurate sentences. With this capability, organizations can potentially avoid miscommunications or inaccurate figures in their reports.
NLG software ensures accurate reporting
Even the most brilliant data scientists occasionally have trouble interpreting data—especially when it comes in large quantities. While machine learning and other areas of data science have helped improve these interpretations, they haven’t helped much with effectively communicating the results… Until now, that is.
With NLG software, massive datasets can be instantly translated into human-readable formats without the need for a data scientist or analyst to sit down and think about how best to put everything into words. With this load of their backs, analysts can spend more time doing their jobs, and management can rest assured that their reports are highly accurate.
When it comes to understanding data, NLG benefits from having access to “labeled” datasets. Here, every piece of data is labeled according to its type or perhaps to certain attributes. This way, NLG software can make valid associations between datasets regardless of their size or complexity.
NLG software eliminates monotony
Many data-driven applications are deployed on massive scales, often having to communicate important statistics hundreds (if not thousands) of times.
For example, a university ranking website could use NLG to translate a database of university data (e.g. admission rates, location, etc.) into human-readable university profiles. Since the ranking website would have to create hundreds of these profiles, hiring human writers to do the task would be inefficient. Instead, NLG software could automatically convert university data into entire articles or, at the very least, short descriptions of each university.
Monotony comes in other forms, too; chatbots, for example, constantly take user requests (data), process them, and then provide a valid and understandable response. The last step (responding) is another example of NLG software at work; where NLP would be responsible for processing the user input, NLG is responsible for processing the chatbot’s output.
The monotony in this case comes from the high volume of user inputs. Since they’re deployed 24/7 on the web, chatbots often take in multiple inputs from multiple users all at once. Where it would take a human staff much more time and effort to respond to these user inputs (i.e. questions), chatbots empowered by NLP and NLG can do so with relative ease.
NLG saves time during analysis
Performing analysis is a major part of the technical writing process, especially for reports and summaries related to data. While most quantitative analyses are completed long before writing the report, qualitative analyses are often left to the writer; in other words, though analytical software may crunch the numbers, writers still have to make sense of what the numbers actually mean.
NLG software is often helpful here. Where a writer might spend time trying to best string different data points into an understandable sentence, NLG software does so instantly and eloquently.
NLG software is an essential bridge between data and humans
Whether it’s used to create data-driven reports or power chatbot responses, NLG software is a powerful tool for converting data into human language. Its uses aren’t limited to just data science, analytics, and chatbots, however; NLG software is quickly making its way into many other areas as well.
Who uses NLG software?
NLG software is used by anyone who needs to convert data into a readable summaries or reports. While this functionality makes NLP software a perfect fit for those in analytical fields, many other users can also benefit from using NLP software.
Business Intelligence and Analytics
NGL software has quickly become an essential tool for business intelligence and analytics, making data more accessible and understandable to everyone throughout the enterprise.
In most business intelligence operations, analytics doesn’t end with analysts; while analysts can get away with understanding data through numbers alone, many members of management and other organizations might need data and numbers put into clear words. While some analysts can do this, NGL software has made the process much easier.
By putting business data into clear words, analysts and management alike are more likely to discover new trends in data—some of which might have gone unnoticed by analysts! With NGL software, data is instantly transformed into narratives and graphs, giving everyone a clear picture and understanding of their organization’s data and trajectory.
This capability also makes business intelligence and analytics more accessible. With NGL software enabling analytics beyond the roles of analysts, everyone can now take part in the business intelligence process.
Finance and Investment Firms
NGL software benefits finance and investment firms similarly to how it benefits business intelligence and analytics. However, graphs and narratives are perhaps even more important in finance, where individuals across many backgrounds and technical abilities rely on financial data to make important decisions.
Graphs and narratives have been crucial to financial reporting for far longer than NGL software has existed. Whether a financial team needs to present quarterly data to management or share stock trends with an investor, graphs and narratives are the key to keeping everyone on the same page. With these tools, one doesn’t need to be a financial analyst to understand complex financial trends; if stock prices are going down, then the graph will show a downward trend (and everyone can share in the panic!).
Before NGL software, however, financial analysts would have to construct these graphs themselves, often producing accompanying financial reports to help non-analysts interpret the graphs. This process was often time consuming and tedious, taking away precious resources from actual analysis.
Plus, producing graphs and narratives “by hand” has become increasingly impractical with the onset of big data and widely-distributed reporting. Today’s management and investors no longer want just quarterly reports, nor do they want to wait to hear about trends; instead, they want all of their data delivered instantly and in real-time, often through a convenient application.
NGL software has been crucial for powering these applications and delivery mechanisms. As financial data is received in real time, NGL software can instantly describe it without a human intermediary. With this ability, applications and financial firms can deliver instantaneous – yet clear – descriptions of financial data.
NGL software has also benefitted financial firms by increasing transparency. Instead of having keeping financial data to a team of analysts, impartial computers can simply display results for everyone to understand. This way, financial data is always presented transparently, and everyone knows what’s going on.
News and Media Companies
While “true” journalism is still a very human effort, basic reporting has become increasingly automated as more publications being reporting on world news. Similarly, media companies have started automating parts of their content production to meet ever-increasing reader demand.
For news media, not every piece needs to be a “hard hitter;” it’s often enough to briefly report on a story, even if that story took place far outside the country. Most news companies are joining this trend, especially as revenues become increasingly dependent on advertisements and, therefore, output.
However, churning out such a large number of reports is easier said than done; without enough staff to meet such requirements, many news companies are using NLG software to offset their workloads. Now, instead of having to “hand-write” stories, NLG software simply takes in news data and weaves it into a serviceable report.
Media companies face a similar challenge, especially those with large web presences. As many readers are now demanding large amounts of content (instantly!), media companies are having a hard time keeping up—especially if their publications are built from a large number of similar pages (such as the university ranking website from earlier).
With NLG software, however, media companies can output articles and summaries without breaking a sweat. This capability has made it possible for even the smallest teams to launch robust, content-rich web presences.
Chatbots and Home Automation
Chatbots and home automation devices (which are technically chatbots) both use NLG software to communicate with their users. Here, NLG software is one half of a chatbot’s functionality; where natural language processing (NLP) software helps convert human language into data, NLP software converts data back into human language.
In a sense, NLG software is a sort of computer-to-human translator!
Typically, most chatbots and home automation devices come pre-built, making it so that end users don’t have to configure much beyond their voice identities. However, enterprise- or research-level chatbots might use custom configurations, allowing engineers to get a “closer look” at how each piece of data is processed. For these configurations, customizable NLG and NLP software are often necessary for building a purpose-built chatbot.
Linguists and Cognitive Scientists
Artificial intelligence is a booming research field, and the sub-category of NLG is no exception. Developing NLG software is a multidisciplinary effort calling upon linguists, cognitive scientists, and engineers to figure out how to assign human “meaning” to data.
Here, NLG software benefits linguists, cognitive scientists, and similar positions in two major ways: 1) researchers help develop cutting-edge AI technology, and 2) NLG software can help researchers reinforce their findings.
While mankind has developed an incredible understanding of the human language, the same cannot be said for the human brain—even when it comes to certain linguistic processes. As a result, NLG software has been an extraordinary helpful research tool, allowing researchers to apply their findings to practical solutions while also giving new insight into cognitive systems.
Anyone or Anything “Translating” Data
You’re probably noticing a common theme throughout these examples: Whether it’s a business report, financial statement, or a chatbot, NLG software converts data into human language. Another application of this function could be an Internet-of-things (IoT) interface, where NLG software would help convert data from the IoT into human-friendly summaries and reports about the “things” in the IoT.
There are, of course, many other applications that benefit from NLG software. However, regardless of the application, most NLG software shares a few common features.
NLG software should be able to take in your organization’s data and integrate with data pipelines. This feature may seem obvious, but it’s important—NLG software, no matter how robust, will be useless to you if it can’t integrate with your current systems!
In most cases, NLG software uses “labeled” data as an input. As a brief recap, data in most machine learning applications (NLG being one) is either “raw” or “labeled.” Structurally speaking, raw and labeled data are exactly the same—the only difference being that labeled data has a “label” categorizing it.
For example, suppose you’re working with university statistics as a data set. This data set might come as a plain text file of university names, admission rates, and so on. While you might be able to recognize these categories, the computer doesn’t—instead, the computer only sees a plain text file! This data set, then, is considered “unlabeled” or “raw.”
If you want to use this data set, you’ll need to tell the computer which data is which. So, you attach “labels” to each area; the admission percentages are labeled as such, as are the university names, and so on. With these labels, the computer (and NLG software) will be capable of stringing together a logical sentence about a university, such as “[university name] has a [admission percentage]% admission rate.”
Now, back to the main point: Your data won’t always come as a plain text file. Since data can take many forms (and often does take multiple forms in most data-driven applications), your NLG software of choice should be capable of taking data in any format. Here, the format is not what’s important—it’s the labeling.
NLG software should present/convert data in way that’s relevant to your uses. The ultimate goal of NLG software is to make sense of data. As a result, it’s crucial for your choice of NLG software to be capable of converting data into language or forms relevant to your organization or uses.
For example: Suppose you’re a part of a media or news company looking to automate content production. Here, you’ll need to take data – such as that about a news event – and convert it into content automatically. While NLG software is the obvious choice for this application, you’ll need a specific type of NLG software to do it right.
For this application, data-to-text NLG software would be ideal. While data-to-text functionality is perhaps the most common amongst NLG software packages, it’s not the only one; some NLG software specializes in producing graphs and charts rather than text. Such a capability wouldn’t be very useful for a news report, but it would definitely be useful for a financial report!
In any case, your NLG software’s output type should align with your organizations needs and goals. Further, the output itself should be presented clearly and accurately—nobody needs nonsense sentences or confusing graphs.
NLG software should be capable of interfacing with NLP software (in some cases). NLG and NLP software are essential for building robust chatbots and human-computer interfaces, such as home automation applications. As a result, NLG software (the output end) should be able to directly interface with NLP software (the input end) for these purposes.
However, a “direct” connection between NLP and NLG software isn’t always necessary. In many cases, they work independently but share the same data pipeline(s). Of course, this situation only emphasizes the importance of data compatibility.
Q: What does NLG mean?
A: Natural Language Generation (NLG) software converts data into human language. In most cases, NLG software provides text-based descriptions of data, making it a useful tool for business intelligence, analytics, and financial reporting.
Q: What is the difference between NLG and NLP?
A: Natural language generation (NLG) and natural language processing (NLP) often work together, but they serve opposite functions: NLP converts human language into data, whereas NLG converts data into human language. Together, NLP and NLG form the foundation of many chatbots and home automation software.
Of course, both NLP and NLG have their own individual uses as well; NLP is often used in voice recognition applications, and NLG is often used to summarize data in human language (such as converting financial data into a financial report). These functions are both useful, but they’re opposites of one another.
Q: What is the difference between NLG, NLP, and NLU?
A: Natural language understanding (NLU) is a specific function of natural language processing (NLP) dealing with the “reading” part of translating human language into data. As a result, NLG has a similar relationship with NLU as it does with NLP—mostly because NLU is simply a part of NLP.
NLU performs several crucial functions of NLP, such as filtering words, sentiment detection (is a sentence “positive” or “negative?”), and topic classification—just to name a few. Thanks to these useful translation functions, NLG software is equipped with better labeled data and can produce more relevant responses.
Q: How do NLP, NLG, and NLU work together? Do they need to be used together?
A: Natural language generation (NLG), natural language processing (NLP), and natural language understanding (NLU) do not always work together, but they often form the foundation of chatbots and home automation applications.
In these cases, NLP and NLU take human language and convert it into data. NLP is the broader category, encompassing all aspects of “processing” human language. NLU is simply a sub-category of NLP, which deals specifically with “reading” functions such as sentiment analysis and paragraph inferencing.
Where NLP and NLU work to understand human language, NLG reverses the process by converting data into human language. Oftentimes, NLP and NLU serve as data “inputs” for NLG, providing some form of command structure for NLG to execute. Once NLG understands the request, it will pull or compute the necessary data and translate it back into human language.
Natural language generation (NLP) software allows you to automatically convert data into text, making it an extremely useful tool for business intelligence, analytics, and financial reports. While NLP is largely used for data-to-text applications, some software packages can also produce graphs and diagrams, and may also work alongside NLU and NLP software in chatbot applications.