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An Overview of Natural Language Generation (NLG)

  • Soumyaa Rawat
  • Oct 27, 2021
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Natural Language Generation


Artificial intelligence technology is a major technological advancement that has benefited mankind worldwide. 


From the retail sector to the educational arena, artificial intelligence algorithms have time and again helped us to make computing processes faster, more efficient, and way more productive. 


That said, several branches of artificial intelligence have emerged over time and diversified their applications with the help of other technologies as well. 


(Must read: Applications of Artificial Intelligence)


One such branch of artificial intelligence is Natural Language Generation (NLG). 


An AI technology that conducts human-to-machine and machine-to-human interaction by deploying written or spoken information from vast datasets, Natural Language Generation in AI is a component of Natural Language Processing. 


Inclusive of computational linguistics,  Natural Language Generation is focused on the conversion of structured data into readable texts for humans. 


With natural language as the building block of this development, NLG is a two-way process that converts structured data into readable texts, and vice versa. 


With the help of trained ML algorithms that transform coded data into readable data, NLG is supported by NLP at its core as it is a sub-type of Natural Language Processing (NLP). Predetermined templates can also be fixed in order to assist the transformation of data.


Invented in the mid-1960s, Natural Language Generation has served as a helpful artificial intelligence and machine learning technique that simply reduces manual labor that goes into interpreting vast amounts of data. 


Rather, it simply converts it into readable data that can be used for various applications. 


“Natural Language Generation, as defined by Artificial Intelligence: Natural Language Processing Fundamentals, is the “process of producing meaningful phrases and sentences in the form of natural language.”

Defining NLG 



How does NLG work


When it comes to the topic - how does NLG work, then it is simply a path of interesting findings that will mesmerize the reader. Let us find out how this concept works. 


A multi-stage process that involves a set of steps to be completed before the actual conversion of structured data into readable data takes place, NLG is an eminent concept in the field of artificial intelligence. 


A set of 6 Natural Language Generation steps are involved in the process. These steps are as follows -

This image enlists the 6 stages of NLG - 1. Content Analysis 2. Data Interpretation 3. Data Structuring 4. Data Summarization 5. Grammatical Construction 6. Language Output

Stages of Natural Language Generation, Source 

  1. Content Analysis 


The first and foremost step in Natural Language Generation is Content Analysis. For any content to be converted into readable information, content analysis is a crucial step that can’t be missed out. 


Similarly, in the case of NLG as well, text analysis of content that is to be converted is important. This involves the deployment of advanced AI Algorithms that study the data and analyze it accordingly. 


  1. Interpretation of Data


Once the important data is recognized and filtered out, the algorithm moves on to the second step of this process - the interpretation of data. 


Data Interpretation refers to the identification of data context and establishing a relationship between the given information. Patterns are identified and trained datasets are used to analyze the same. 


Moreover, interpreting the data also involves putting it together piece by piece so that the structuring can be efficient.  


  1. Structuring of Data


The third stage - data structuring, is an essential stage of NLG that is often supported by trained algorithms refers to structuring data in a way that is understandable by humans. 


Herein, a narrative is established and a document structure is planned out for putting together the data in an interpretative format. 


Structuring the data is often an essential step as it helps the generation of natural language flow easily and efficiently. 


(More to learn: NLP guide for beginners


  1. Data Summarization


The next step, data summarization involves selecting the most relevant parts of the analyzed data and placing them in the structure to summarize the data without missing out on anything crucial. 


Data summarization, like any other stage, is extremely important and involves data to be presented in the best way. Sentences are joined and the relationship between different sub-topics is established for the flow to be uninterrupted. 


  1. Grammatical Construction


While data summarization is done while joining sentences that have the same context, grammatical construction is also important for readability by all. Imagine reading a document without any punctuation marks or a defined pattern. 


That wouldn’t go well, right? Similarly, Natural Language Generation also employs this stage to construct grammatical formation of the data in a readable way.


  1. Language Output


The last stage of the NLG process is language output. This refers to the output of the whole process that is readable language structured into a document with all necessary information extracted from a massive length of data. 


The language output is now ready for humans to read, understand, and interpret, as and when they wish to. 


(Must Read - Benefits of NLP)





As we have already read that Natural Language Generation is a component of Natural Language Processing in AI, it is worth noticing that the two concepts are distinct in several ways. 


The first and foremost difference between these 2 concepts is that while NLG refers to the conversion of structured data into readable information, NLP involves the conversion of readable text into structured data. 


In a way, NLG generates natural language out of the ‘processed’ data and perhaps this is the biggest difference between the two concepts. 


Simply put, NLP is what happens when computers read language whereas NLG indicates the concept of computers writing language. 


“NLG can be defined as collecting raw text data and turning them into a human narrative. NLG is related to Natural Language Processing (NLP), but works in the opposite direction. NLP collects data from text; NLG generates text from data.”

Difference between NLG and NLP 


However, it must not be understood that the two concepts are completely unrelated. Rather, NLG acts as an engine of NLP that together makes the conversion of raw text into computable data, or vice versa possible. Even when computers read or write the language, they employ similar steps in order to get the desired results. 



Applications of NLG 


Even though we have discovered that NLG is quite helpful in the domain of AI in our day-to-day lives and data computing, we will now be discovering the applications of the Natural Language Generation in this segment. 


  1. Chatbot Communication


Out of all the applications of NLG, the most notable use of this technique can be observed in the Chatbot communication process that helps robots communicate with humans in natural language. 


Even though robots and humans cannot communicate alike, it is still possible that chatbots have been communicating with customers of e-commerce platforms and other such facilities, thanks to Natural Language Generation. 


That said, robots write the language by converting computed data into human language, which enables them to address customer problems and deliver related services.


  1. Content Creation


Another use of NLG is content creation that helps businesses and organizations to convert statistical data into readable content for humans. 


With this technology, computers can create understandable content for humans to interpret, unlike statistical data that is hard to interpret by laymen. 


This way, content creation is one of the biggest applications of text generation with the help of NLG as it saves time for companies to break down content and put it together in easy language. 


Perhaps it is significant to note that content creation can benefit businesses and corporations to employ NLG and generate content for various purposes - mass emails, customer notices, etc. 


  1. Performance Analysis Reports


Another one of the most prominent uses of NLG is performance analysis reports. Creating reports has been and still is one of the most tiresome tasks for humans to accomplish. 


However, thanks to NLG, this is no longer their burden! To clarify your mayhem, NLG converts computed data into readable texts that are the basis of creating detailed reports. 


In essence, NLG algorithms can readily convert computed data into well-informed analysis reports that not only summarize the performance of a business/ individual/ or any organization over a period of time but also efficiently deliver detailed analysis without spending too much time. 


It is, therefore, a time-efficient and rapid tool to create analysis reports.   



Summing Up 


To sum up, Natural Language Generation has been in use since the 1960s with the help of ever-evolving mechanisms in the artificial intelligence arena. 


Needless to say, NLG has many benefits that can eventually replace manual labor in terms of converting computed data into readable text. 


(Suggested blog: NLU Tutorial)


With so many applications in our day-to-day lives, NLG is a sub-type of NLP that works rather inversely. Yet, the two concepts have time and again served as highly useful techniques that can bring down human effort and make the process more efficient and productive. 


All in all, it can be defined as the reverse process of NLP that helps computers to write natural language by generating readable text. 

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    Oct 27, 2021