Dispelling the Biggest Myths ofNatural Language Generation

W H I T E P A P E R

Dispelling the Biggest Myths of Natural Language Generation 2

The “automation is costing jobs” storyline has become a popular media topic in recent years.There’s been a steady drumbeat of articles and news segments warning us that entire industriesare quickly turning to automation in lieu of a human workforce. Whether it’s self-driving truckstransporting goods or robots overseeing an assembly line, automation will continue to permanently alter jobs that humans have held for generations.

NLG, however, is having a different impact on jobs. NLG is augmenting the roles of employees by freeing up their time to focus on other ROI-generating tasks. Even traditional content-generating roles like copywriters, analysts, and journalists are expanding their roles because of their ability to generate narratives and stories with a reach, depth, and speed not possible by hand. Likewise, we have seen some of our clients actually create new jobs based around NLG technology.

“Do you use actual robots to write your stories?”

No, unless you count laptops as “robots."

And yes, we’ve been asked this question before. Several times, in fact.

While this specific example may seem absurd, it’s completely fair to expect some confusion around what it is we do at Automated Insights. Natural Language Generation, or NLG, is evolving at a faster clip than most casual observers can follow. We operate in an industry where terminology and labels are still being defined and formed. Analysts are still rushing to fully understand and map out the various offerings that make up the NLG landscape. Meanwhile, competitors are racing to stake their claim to both technological advances and the right nomenclature that set themselves apart from the pack.

So, as you can see, it really is no wonder we receive so many questions.

This whitepaper is intended to answer a few of the most common questions we receive by debunking some of the myths infiltrating the market. Our team of NLG specialists narrowed the list down to the two biggest misconceptions out there. Let’s get started.

MythNLG is coming for your job

Introduction

3Dispelling the Biggest Myths of Natural Language Generation

“Internally, the reaction has been positive from staff, largely because automation has freed up valuable reporting time and reduced the amount of data-processing type work they had been doing.

Philana Patterson, Assistant Business Editor at the Associated Press

Empowering the Human WorkforceNLG isn’t taking away people’s jobs—it’s helping people do more in their roles. For instance,Automated Insights’ Wordsmith platform allows the Associated Press to publish 4,400 corporate earnings stories per quarter, up from 300 manually-written stories. Automation simply allows these writers to highlight 4,100 more organizations than they ever could before.1

Not surprisingly, most of the writers and analysts with whom we speak are excited about NLG’s impact on their jobs. Alex Wilhelm at TechCrunch quipped, “Given how much I enjoy covering every semi-annual earnings season, I’m downright excited about the development (of NLG). Earningsseason makes most reporters want to poke their eyes out with sharp objects.”3

The figure to the left plots fraction of firms’ earnings announcements receiving an AP reporter-written and automated article, by quarter. The sample includes 4,292 firms and 57,467 earnings announcements.

AP Reporter-Written and Automated Earnings Reports

80%

70%

60%

50%

40%

30%

20%

10%

0%

2012 Q1

2012 Q2

2012 Q3

2012 Q4

2013 Q1

2013 Q2

2013 Q3

2013 Q4

2014 Q1

2014 Q2

2014 Q3

2015 Q1

2015 Q2

2015 Q3

2014 Q4

% of earnings announcements with report-written article

% of earnings announcements with automated article

% EarningsAnnouncementsCovered

Based on a sample of 4,292 U.S. Public Companies

*Automated Insights enables reporters with the AP to produce 4,400 quarterlyearnings stories–a 15-fold increase over its manual efforts.2

Changing the ConversationNLG is also having a positive impact within industries where extracting insights from massive amounts of data is vital to the success of a company. NLG produces valuable, easy-to-understand narratives that provide insight for executives making critical business decisions. Constellation Brands, a distributor of premium wine and beer, understands exactly how important it is for team members to be able to effectively manage data. Adding NLG to their business intelligence dashboards gave the company’s data analysts the power to move beyond dissecting and translating data for reporting to focusing more on their plans for action derived from their insights.

File Edit View Insert Tools Help

Agency Sales Grocery JDE Direct Delivery LCBO Licensee Sales Other Sales Regular OTC Sales The Beer Store

Market Previous 1 2 3 4 5 Next

Constellation Brands - TIBCO Spotfire

Legend:

Selectors - General:

Sales Parameter:

Measure Select:

Calendar Type:

Time Periods:

Segment:

Selectors - Product Attributes:

Class:

Import Status:

Vintages:

New / Old World:

Country of Origin:

Market PerformanceRY - CLOSED MONTH - $

Overall TrendsThe Wine segment has seen positive revenue growth of 5.5% in the RY period. Constellation has seen strong revenue growth of 9.8% for the period, outpacing the competition’s positive growth of 4.7% in the segment.

Trends Over TimeThe market growth in the segment is being driven by R3 at 6.9%. Constellation Brands is driving the market as a result of faster growth in R3 at 12.0%.

Channel TrendsConstellation Brands’ revenue growth is powered predominantly by Regular OTC Sales (+$26.51 million), LCBO Licensee Sales (+$2.03 million), and Grocery (+$923,778), where Cbrands is up against competitive revenue growth of $71.75 million, $3.30 million, and $7.43 million, respectively.

Attribute Level Holding Company Level SKU Level Rep Territory Level Top & Bottom StoresMarket Overview

$ Sales$ Sales G

rowth

WINE Market by Time Periods

WINE Market by Channel

CP CPFYT CPR03 CPR06 CPR13

WINE

$ SALES

SALES

6.3M 1.9M

2.414.0

110.9M 20.2M

1,677.6M

316.5M

1,950.8M

368.2M537.6M99.6M

978.3M

184.8M

-11.0 -10.0

3.07.0

3.98.7

3.8

9.14.4

7.7

5.9M 823.4k 34.5M2.4M 6.4M

0.917.3 6.9

125.9k 3.7k

2.8 8.0

490.5M

88.1M208.5k

-80.8-90.7

LCBO Periods

R3 - CLOSED PERIOD

Type to search in list

(All) 10 valuesDESSERT WINEFORTIFIEDICEWINE

SPIRITREFRESHMENTSUNASSIGNED

DOMESTICIMPORT

General DeptVintages Dept

NEW WORLDOLD WORLD

Type to search in list

(All) 47 valuesITALYUNITED STATESCANADA

$ Sales$ Sales G

rowth

Refresh

*Constellation Brands uses natural language generation to automatically produce summaries of salesperformance across regions and brands directly within TIBCO Spotfire.

One question that we periodically hear from new users is “what is Advanced NLG?” NLG is still a recent technology, so it’s not surprising that there are questions about the various terminology that solution providers throw around.

According to Business Insights Manager Sasha Teska, “[NLG] has allowed me to replace my former role of reporting and tracking with new aspects that are more valuable to my company.” These positive changes have actually allowed roles and responsibilities to scale skill sets across multiple teams. Teska noted, “Instead of asking ‘What is happening,’ our teams have evolved to ‘What are we going to do about it?’”

Myth“Advanced” NLG

3Dispelling the Biggest Myths of Natural Language Generation

4Dispelling the Biggest Myths of Natural Language Generation

The simple answer is that Advanced NLG is a marketing term typically applied to a canned, static, or “black box,” NLG solution. These solutions are known for disappearing with your data for weeks or months at a time and returning with narrative that you’re not able to directly edit. What’s worse, is the fact that non-domain experts are handling your data and trying to produce a narrative. Since they are not experts in the data which they are building out the rules for, you arrive at a finished output that showcases very rudimentary insights.

Data changes constantly, so your ability to quickly edit and update within an NLG platform such as Wordsmith is quintessential to a successful and valuable NLG deployment. “Advanced” NLG solutions like to tout that they save you time by removing your direct involvement. In reality, you spend more time submitting change orders to make even the smallest of edits to the typically generic output. For a solution that claims to be advanced, shouldn’t it give you more power and control over your content?

“Advanced NLG” is simply a marketing term typically applied to a canned, static, or “black box” NLG solution.

• Known for disappearingwith your data for months

• Non-domain expertshandling your data/outputs

• Not able to quickly updateor edit outputs

• Rudimentary insights likemin, max, sum, average

Company A is a small company in an entirely new field. The stock prices of organizations like this tend to fluctuate quite dramatically. In financial parlance, this is referred to as volatility.

Company B, on the other hand, has been publicly traded for 70+ years, operates in amature market, pays a regular dividend, and has generated stable revenue. Company B istraditionally referred to as a blue chip stock whose stock price tends to be very steady with minimal volatility.

What does Advanced NLG mean for data-rich industries?When it comes to data-rich industries like business intelligence, financial services, and healthcare, “Advanced” NLG solutions tend to lack awareness of the underlying dataset. They rely on apredefined set of rules and conditions that parse the surface of your data to extract basic talking points in a pre-set formulaic manner. These outputs typically include very basic metrics such as min, max, sum, and average. While these metrics provide slight value, the key is the ability to analyze data behind the entirety of your dashboards and tell a story across your visualizations. Furthermore, by removing any control you may have over the contextual layer (the step between processing data and generating output), “Advanced” NLG is forced to interpret fields of data in the same manner every time, no matter the industry or use case.

Let’s review a common example from the financial services industry that would stymie the approach of an “Advanced” NLG solution:

5Dispelling the Biggest Myths of Natural Language Generation

“Advanced” NLG would look at these two stocks and, without any input or guidance, point out that Company A has a lot of variance while Company B does not. However, this approach isfundamentally flawed.

First, you already know that, due to its nature, Company A will have much higher variance so the information is entirely superfluous. Second, notice the subtle shift from using “volatility” to “variance” over the past few sentences. This shift is deliberate, as “Advanced” NLG solutions incorrectlyidentify every dataset as being equivalent. Without any guidance or consideration for terminology at the contextual layer, these solutions are unable to use company or industry-specific expressions and descriptions. In the context of NLG, that functionality should be considered basic.

As an NLG pioneer, Automated Insights has received feedback over the years from clients like the Associated Press, Edmunds, Yahoo!, and countless others that led us to build Wordsmith, a solution that puts you in complete control of your content. Additionally, the most important lesson that we learned is that you are the expert on your data. You know the story that it should tell and you know how to tell that story better than anyone. So remember, despite what a team of marketers want you to believe, “Advanced” NLG is just a blanketed approach that tries to generate content in a vacuum. Without any of the context that makes your data and story unique, the narrative you generate will always fall short.

We hope that this whitepaper has provided some clarity into some of the most commonmisconceptions surrounding NLG. If you would like to learn more about NLG or how Wordsmithhelps its clients, please visit www.automatedinsights.com.

Request a DemoSchedule your personal demo today and see how NLG solutions can grow your business.

Request Demo

Questions?

6Dispelling the Biggest Myths of Natural Language Generation

Resources1. Capital Market Effects of Media Synthesis and Dissemination: Evidence from Robo-Journalism

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2872784

2. Study: News automation by AP increases trading in financial markets

https://insights.ap.org/industry-trends/study-news-automation-by-ap-increases-trading-in-financial-markets

3. Case Study: Automated Insights and the Associated Press

https://automatedinsights.com/case-studies/associated-press

About Automated InsightsAutomated Insights (Ai) is the creator of Wordsmith, the world’s first self-service natural language generation platform for the enterprise. Automated Insights empowers organizations to generate human-sounding narratives from data, making it easy to produce real-time, written analytics, personalized reports, and stories at scale. The Wordsmith platform is utilized by companies and partners, including the Associated Press, Cisco, MicroStrategy, NVIDIA, Tableau, TIBCO, and Qlik, in over 50 data-driven industries, such as business intelligence, financial services, ecommerce, sports and entertainment, and media. For more information, visit automatedinsights.com.

Dispelling the Biggest Myths of Natural Language Generation