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Natural language generation (NLG) has emerged as a crucial application for text content automation as industries continue to adopt artificial intelligence (AI) to enhance their operations. NLG is a class of computer program that transforms structured data into accessible human language, including sentences, paragraphs, and complete articles.

The value of NLG is found in its capacity to evaluate and understand data in order to produce content that is not only educational but also interesting to read. Utilizing NLG, organizations can quickly create massive amounts of information that is tailored to their unique requirements, assuring accuracy and consistency while saving time and money.

Data analysis, interpretation, and conversion into a comprehensible written format are only a few of the phases that make up the NLG process. As this technology has advanced, NLG systems can now create high-quality material with improved accuracy, relevance, and context.

Despite its many advantages, NLG still has several limitations, including the necessity for high-quality data and the capacity to produce accurate and interesting information. There are ethical issues that must also be taken into account, such as the chance that NLG will provide biased or misleading content.

NLG, however, has a wide range of uses. NLG is used by businesses in the financial, healthcare, and e-commerce sectors among others to create reports, product descriptions, and customer communications. NLG can also be used to write news stories, textbooks, and even movie scripts in fields including journalism, education, and entertainment.

NLG is a potent technology that is fundamentally altering how companies and organizations create written content. NLG has the potential to transform numerous sectors and make written communication more accessible and efficient than ever before by automating the content generation process and enhancing the output quality.

How significant is Natural Language Generation?

Before choosing which things to purchase, about 35% of shoppers visit blogs and websites. It is challenging for many retail and e-commerce businesses to manually create material for each product. This process can be automated using NLG technology. Consequently, enhance the businesses’ entire marketing and sales efforts.

There is potential in the NLG market for a number of reasons, including:

1. Demand for personalized: Demand for personalized content is rising, and NLG offers organizations an affordable solution to deliver highly targeted content at scale as customers demand more personalized and relevant information.
2. Big data growth: Due to the massive increase of data, businesses are seeking for ways to use this information for analysis and decision-making. NLG offers a mechanism to use written reports and summaries to translate this data into useful insights.
3. Time and money savings: By automating content development procedures, firms can cut the time and expenses involved in manual content generation.
4. Enhanced customer engagement: NLG can assist organizations in enhancing consumer engagement and satisfaction by creating interesting and educational content.
5. AI and machine learning developments: As these fields continue to evolve, NLG systems get more complex, allowing them to produce high-quality material with improved accuracy and relevance.

Overall, the NLG market is expected to expand as more companies attempt to use AI technology to enhance their content development workflows and provide consumers with more tailored, engaging content.

How does NLG function?

Using the example of a robot journalist producing news about a football game, we can divide the explanation of an automated text generation process into six stages:

1.Choosing the Content
Content determination is the initial step in an automated text generating process, during which the pertinent material is found and extracted from the source files. Setting content limitations is crucial for ensuring that the resulting material is succinct and appropriate for the target audience.

The information gathered from live feeds or databases in the context of football news may include additional data that is not required for the news story. The NLG method can concentrate on producing content that is educational and interesting by identifying the important aspects of the game that will be of interest to readers, such as goals, cards, and penalties. By ensuring that the generated news piece is matched to the needs and tastes of the target audience, this helps to increase the quality and relevancy of the product.

2. Interpretation of data
Data interpretation, which is the second stage of an automated text creation process, entails contextualizing the studied data and identifying pertinent patterns using machine learning algorithms. Finding the core ideas and concepts that will serve as the foundation for the generated text is crucial at this stage.

Data interpretation in the context of football news entails locating the pertinent details, such as the winning team, the goal scorers and assisters, and the timing of the goals. The NLG system can produce text that offers the reader new insights and value by placing this material in context and identifying patterns in the processed data.

The NLG system can use machine learning techniques to find pertinent patterns in the data, such as trends in team performance, player statistics, and match statistics. The NLG system can produce text that is more useful and interesting by interpreting the data in this way, which raises the quality of the output and improves the reader’s experience.

3. document preparation
Document planning, which is the third stage of an automated text production process, is where the NLG system arranges the data structures into a logical narrative structure. The plan for the generated text must be developed during this step in order to be logical, interesting, and simple to read.

Document planning in the context of football news entails arranging the generated material into a structure that follows a narrative flow. Football news articles typically start with an introduction paragraph that lists the final score and makes a statement about the level of intensity and competition. The pre-game standings of the teams are highlighted after this, and other significant game moments are discussed in the paragraphs that follow. Interviews with players and coaches may also be used to wrap up the article.

The narrative framework of the resulting text is planned by the NLG system using the structured data from the previous step. The NLG system can make certain that the generated content is interesting and educational for readers by arranging the material into a narrative framework that is simple to follow. This helps to raise the output’s general quality and enriches the reading experience.

4. Sentence Compounding
A critical step in the automated text generating process is sentence aggregation, sometimes referred to as micro planning. To make sure that the generated text is understandable, succinct, and pertinent to the end-user, it requires choosing the best words and expressions for each sentence. Producing high-quality content that successfully communicates the desired information to the reader requires this approach.

For instance, phrase aggregation in the context of football news may entail joining separate sentences regarding a particular player’s performance to produce a longer and more detailed sentence. Sentence aggregation might look like this:

Original phrases:

In the thirty-second minute, the guy scored a goal.
In the 70th minute, the player contributed to another goal.
Summarized sentence

In the 30th and 70th minutes, respectively, the player assisted on two goals.
Another illustration would be to combine different team performance sentences into one cohesive, informative paragraph. For this, sentence aggregation might look like this:

Original phrases:

All game long, the team had the most of the possession.
The team was given numerous chances to score but didn’t take advantage of them.
Collectively speaking

The team held the majority of the possession during the game and had numerous scoring opportunities, but they did not take advantage of them.
Generally speaking, sentence aggregation is a crucial step in the automated text creation process that ensures the created material is understandable, succinct, and pertinent to the reader.

5. Grammaticalization
Grammaticalization, the fifth stage of an automated text production process, entails making sure that the resulting content follows proper grammar, spelling, and punctuation. At this stage, the text itself is validated to make sure it complies with the syntax, morphology, and spelling criteria.

Grammaticalization in the context of football news makes sure that the resulting content is written in the proper tense, which is often the past tense. This makes it easier to make sure that the generated text is accurate, comprehensible, and clear to the reader. The NLG system also ensures that the material is grammatically sound and understandable by checking for proper sentence construction, spelling, and punctuation.

The NLG system can guarantee that the output is of the highest caliber and complies with the necessary requirements by checking the generated text for grammatical accuracy. The generated content’s professionalism and credibility are enhanced, which is important for winning readers’ trust.

6. Implementation of Language
Language implementation is the last step in an automated text generating process, when the created text is entered into templates and output in the required format in accordance with the user’s preferences. This step is essential to ensuring that the generated text is presented in a simple and straightforward manner.

The NLG system ensures that it is consistent with the user’s preferences at this stage by formatting the generated text into the specified output format, such as HTML, PDF, or plain text. To ensure that the generated text is presented in a polished and visually appealing way, the NLG system may also integrate it into pre-designed templates.

The NLG system may make sure that the output is user-friendly and fulfills the needs and expectations of the target audience by implementing the generated text in the appropriate format and in accordance with the user’s preferences. This makes it more likely that the created text will be well-received and fulfill its intended function.

What are the top 7 uses for Natural Language Generation?

NLG technology is made to convert structured data into insights that can be understood by humans, making it useful in a variety of contexts related to reporting, content development, and content personalisation. This adaptable technology has the power to completely change how companies and organizations create written material.

Finance, healthcare, e-commerce, journalism, education, and entertainment are just a few of the fields where NLG can be used. NLG can be used to produce financial reports and investment summaries in the financial sector, as well as patient reports and medical records in the healthcare sector.

NLG can be used in e-commerce to create product descriptions and tailored buying suggestions. NLG can also be used in journalism to create news pieces and summaries of breaking news. NLG can be used to produce textbooks and other instructional resources in the field of education.

In general, NLG technology is widely applicable and can be utilized in any sector where data needs to be translated into insights that can be understood by humans. NLG is a formidable technology that has the potential to alter numerous sectors and make written communication more accessible and effective than ever before. It can automate content creation processes and increase the quality of the output.

1. Purchase and Sale
Retailers and wholesalers are changing how they maintain and develop product descriptions and personalize customer communications thanks to NLG technology. For e-commerce and online shopping platforms, NLG solutions may offer precise and interesting product descriptions and classification, while also enhancing the user experience through the use of chatbots.

NLG technologies like AX Semantics, according to Steven Morell, CRO of AX Semantics, can automate the process of producing product descriptions for e-commerce websites. Businesses can quickly and cost-effectively produce massive volumes of product descriptions by utilizing NLG technology. These product descriptions can be altered to meet the unique requirements of the intended market, making for a more specialized shopping experience.

NLG can be used to personalize client communication in addition to product descriptions by utilizing chatbots. By responding to frequently asked inquiries and resolving client difficulties in real-time, chatbots can offer immediate customer care and help. Chatbots can be programmed to answer in a natural and interesting way using NLG technology, which enhances the general customer experience and satisfaction.

By enhancing the effectiveness and caliber of product descriptions and customizing client interactions through chatbots, NLG technology has huge potential to disrupt the retail and wholesale sectors. NLG is a technology that is becoming more and more crucial for attaining this goal as more firms seek to improve their internet visibility.

2. Finance & Banking
In the banking and finance sector, where data and insights are crucial for performance reporting and decision-making, NLG technology is playing a big role. NLG systems can help with the analysis of financial data and produce useful insights for firms in addition to producing automatic profit and loss reports.

Additionally, NLG methods are being utilized to assist fintech chatbots that converse with clients and offer tailored financial management guidance. Customers can use these chatbots to assist with a variety of financial chores, including budgeting, cost tracking, retirement planning, and investment advising.

Banks and other financial institutions can use NLG technology to automate a number of their reporting and analytical procedures, freeing up valuable resources and enabling personnel to concentrate on more strategic activities. This could boost the organization’s general performance, increase efficiency, and cut expenditures.

By providing precise and consistent documentation of financial transactions and other regulatory obligations, NLG technology can also help with compliance reporting. This can lessen the possibility of fines and legal troubles by ensuring that firms are abiding by the necessary laws and standards.

Overall, NLG technology is revolutionizing the banking and financial sector by offering insightful data, streamlining reporting and analytical procedures, and improving customer service with individualized chatbots. NLG is quickly becoming a crucial piece of technology for banks and other financial institutions looking to maintain their competitiveness in an increasingly digital market thanks to its capacity to manage massive volumes of financial data and produce insights in real-time.

3. Manufacturing
In the manufacturing sector, where IoT applications are producing enormous amounts of data that can be used for performance optimization and maintenance, NLG technology is becoming more widely deployed. NLG can assist staff members act more quickly by automating the sharing of significant discoveries, increasing productivity and decreasing downtime.

IoT devices produce a substantial amount of data that may be used to monitor equipment performance and spot potential maintenance concerns as they become more commonly deployed in production facilities. NLG can be used to automatically notify IoT device status and maintenance alerts, making sure that staff members are made aware of problems in real-time and are able to take immediate action.

Additionally, key performance indicators (KPIs) for manufacturing processes may be analyzed and reported on using NLG, giving important insights into production efficiency and quality assurance. NLG can assist companies in identifying areas for improvement and streamlining their production processes for optimal efficiency and profitability by automating KPI reporting.

Additionally, NLG can be used to automate the reporting of safety data and compliance information, ensuring that staff members are made aware of any dangers or safety concerns as well as that the business is in compliance with all applicable laws and standards.

NLG technology is fundamentally changing the manufacturing sector by automating reporting procedures and delivering insightful data that can be used to boost productivity, save downtime, and optimize production methods. NLG is quickly becoming a crucial piece of technology for companies looking to stay competitive in a market that is rapidly changing thanks to its capacity to manage massive amounts of data and produce insights in real-time.

4. Media
The automation of content generation and assistance with summary provided by NLG technology are revolutionizing the media sector. Specifically, NLG solutions are being utilized to produce “robot journalists,” often known as sports and financial news reports, employing templates and natural language generation methods.

Utilizing NLG technology, news items may be created rapidly and efficiently while maintaining accurate and interesting information. NLG can be used to condense complex material, making it easier for readers to acquire and comprehend.

NLG can also be used to customise material for certain readers, resulting in a more interesting and customized experience. NLG systems may provide content that is engaging and relevant to each reader by examining user data and preferences, increasing engagement and satisfaction.

Refer to associated articles that offer in-depth insights into this quickly developing topic for more details on robot journalists and other AI uses in media.

In general, NLG technology is transforming the media sector by automating content generation and offering readers individualized experiences. NLG is quickly emerging as a critical technology for companies looking to maintain their competitiveness in the increasingly digital marketplace thanks to its capacity to manage massive volumes of data and produce insights in real-time.

5. Insurance
NLG technologies are being used in the insurance sector to enhance client service and develop individualized policies. Insurance companies can produce succinct and understandable descriptions of insurance plans, policies, and benefits using NLG technology, making it simpler for customers to comprehend and select the appropriate coverage.

NLG can also be used to tailor insurance policies to the unique requirements and tastes of particular clients. NLG systems can create customised insurance plans that provide the proper level of coverage at a reasonable price by examining consumer data and preferences.

The automation of claims processing and other administrative duties using NLG technology can also increase efficiency and lower costs for insurers. Insurance companies can improve customer satisfaction and retention by focusing more on customer service and other value-added operations by automating these services.

Overall, NLG technology is revolutionizing the insurance sector by enhancing customer service and developing individualized plans. NLG is quickly emerging as a critical technology for insurers looking to remain competitive in a market that is becoming more and more customer-focused thanks to its capacity to automate administrative duties and offer individualized services.

6. Transportation
NLG technology is being used in the transportation sector to improve customer satisfaction and communication. By using chatbots that are driven by NLG technology, passengers can receive real-time updates regarding delays and schedule changes, decreasing annoyance.

Additionally, passengers can receive customised and simple-to-read travel plans using NLG technologies, giving them all the information they require to efficiently navigate their route. These itinerary details, including departure and arrival timings, ticket information, and pertinent driving directions, can be changed.

Additionally, by analyzing trip data, NLG technology can produce insights into consumer behavior, preferences, and trends. Transport firms can improve customer happiness and loyalty by studying this data to optimize their services to better match customer demands and expectations.

Overall, NLG technology is revolutionizing the transportation sector by enhancing customer service and communication. NLG is quickly becoming a critical piece of technology for transportation firms looking to stay competitive and satisfy the demands of today’s tech-savvy clients because to its capacity to automate administrative operations, deliver real-time warnings and personalized services, and provide.

7. Politics
The employment of NLG solutions in the political sphere involves a sizable danger of disseminating individualized propaganda and misinformation. The existing flow of political misinformation has the potential to be amplified by NLG technology, making it much more pernicious and individualized.

With the use of NLG technology, political organizations may create tailored material that appeals to particular target audiences, raising the possibility that they will interact with it and be swayed by its messaging. By spreading misinformation, propaganda, and other damaging materials, this may influence public opinion and threaten the democratic process.

NLG can also be used to produce deepfakes and other kinds of modified content that can be used to fool or mislead the general audience. Using realistic-looking movies and images to distribute fake information and influence public perception is now simpler than ever thanks to technology.

Overall, there is a huge risk of propagating propaganda and false information when using NLG solutions in politics, which can seriously harm the democratic process. Policymakers and the general public must be on guard as this technology spreads and take action to guarantee that it is used sensibly and morally.

What examples of Content Automation in the real world have NLG enabled?

Here are a few instances of actual content automation using NLG:

• OpenAI’s most recent language model, GPT-4, is an eagerly anticipated follow-up to the wildly popular GPT-4. As part of its development, OpenAI published a piece titled “Robots Come in Peace,” authored by GPT-4, displaying its aptitude for producing excellent English.

While GPT-4 has demonstrated a great capacity for producing narratives that are well-written, it lacks the logical comprehension necessary to assure the veracity of its articles. Because of this, some articles are prone to mistakes, which can harm their credibility and value as a whole.

With GPT-4, OpenAI hopes to tackle this problem by adding cutting-edge methods for context-based comprehension and logical reasoning. This will make it possible for GPT-4 to provide content that is more accurate, trustworthy, and up to the high standards needed for use in practical applications.

Despite these developments, it’s crucial to remember that NLG technology is still in its infancy and still has a long way to go before it can be used ethically and accurately. It will be crucial to stay alert as technology develops, come up with solutions to these problems, and make sure that NLG-generated information is trustworthy, moral, and beneficial.

• Google’s newest language model for dialogue applications, LaMDA, was introduced in the middle of 2021. LaMDA is a highly developed AI that can hold sophisticated and realistic-sounding conversations since it has been trained on massive amounts of data.

LaMDA was introduced to the world by Google through two demonstrations in which it purported to be a paper airplane and the planet Pluto. LaMDA exhibited its capacity to comprehend context, offer pertinent responses, and engage in lively, human-like dialogue in both demos.

With LaMDA, Google is expanding the capabilities of NLG technology and creating new opportunities for dialogue-based applications in a variety of fields, including personal assistants and customer support. LaMDA and other NLG technologies are expected to see even more spectacular uses in the years to come as technology progresses.

• Wu-Dao, China’s advanced language model, has been trained on 4.9 terabytes of excellent images and texts in both Chinese and English. It is referred to be a “improved” version of GPT-4. Wu-Dao is a very adaptable NLG tool thanks to its outstanding capabilities, which allow it to produce both text and graphics.

Wu-Dao was exposed to the public as a virtual student who could create music, draw, and write poetry in order to demonstrate its capabilities. This demonstration demonstrated the model’s amazing capacity for creativity as well as its capacity to comprehend and make sense of intricate verbal and visual information.

China is making tremendous advancements in NLG technology with Wu-Dao, and there are numerous possible uses for this cutting-edge device. We can anticipate seeing even more stunning demonstrations of the technology’s capabilities as well as an expanding number of applications in industries like media, marketing, and customer service as it continues to develop and advance.

• In 2019, the renowned academic publisher Springer made history by releasing the industry’s first machine-generated book. This significant accomplishment showed how NLG technology has the ability to completely transform the publishing sector by automating the production of top-notch content.

The book, “Lithium-Ion Batteries: A Machine-Generated Summary of Current Research,” was produced by combining NLG algorithms with machine learning methods. Because of how precise and educational the final product was, NLG technology has the ability to completely alter how we produce and use knowledge.

We may anticipate seeing much more amazing applications of this ground-breaking technology in the world of publishing and beyond as NLG technology advances and is more extensively used.

• The Smart Compose feature in Gmail is a useful tool that offers users personalized suggestions for what to write in an email. Smart Compose can suggest pertinent phrases and even finish sentences by assessing the content of your email and your writing style.

As you use Smart Compose, the algorithm improves its suggestions for upcoming emails by taking into account your choices and adapting to your writing style. You may create emails more quickly and effectively as a result of the tool becoming more precise and tailored with time.

One example of how NLG technology is revolutionizing how we communicate and engage with technology is Smart Compose. We can anticipate seeing much more complex applications of NLG technology in our daily lives as these tools grow more powerful and widely used.

• The very unique paraphrase tool QuillBot enables users to rapidly and easily produce high-quality text by utilizing the power of NLG. In order to produce precise and interesting paraphrases that are grammatically sound and simple to read, the tool uses sophisticated algorithms to assess the context and meaning of text.

QuillBot includes a variety of other features in addition to its paraphrasing capabilities that are intended to improve the content creation process. These include the capacity to rearrange sentences and paragraphs, offer word selection advice, and create summaries and outlines out of lengthy texts.

Because of its powerful NLG technology, QuillBot is currently a well-liked option for authors, students, and professionals who want to produce high-quality content quickly and effectively. We can anticipate seeing much more spectacular applications of NLG in the realm of content creation and beyond as the technology continues to develop and improve.

• Excellent examples of how NLG technology is changing how we engage with technology include conversational AI and chatbot apps. These sophisticated technologies analyze and respond to user inquiries and requests in a way that seems intuitive and natural thanks to natural language processing and generation capabilities.

Chatbots and conversational AI systems are crucial tools for customer service, personal assistants, and other applications because they can produce correct and pertinent responses in real-time by evaluating the context and meaning of human input.

In addition to its useful uses, chatbots and conversational AI systems mark an important turning point in the advancement of NLG technology by highlighting how this technology might enhance the way people communicate with both machines and one another. We may anticipate seeing even more spectacular uses of NLG in the area of conversational AI and beyond as these technologies continue to develop and improve.

News

1. A prominent media outlet that has been in the forefront of implementing NLG technology is The Associated Press (AP). The AP has been automating the production of corporate earnings reports in recent years using NLG, showcasing the capability of this technology to simplify challenging reporting jobs.

The AP can produce thorough earnings reports based on structured financial data quickly and correctly with NLG, giving readers up-to-the-minute information on corporate performance. The firm is now able to produce content more effectively and economically, freeing up resources for other crucial activities.

A notable development in journalism, the AP’s use of NLG in corporate earnings reporting exemplifies how technology can change how news is produced and received. We may anticipate seeing even more spectacular applications of NLG technology in the media sector and elsewhere as it continues to develop and get better.

2. For its creative application of NLG technology in its reporting process, The Washington Post has been in the news. The Post has created an internal automated storytelling tool called Heliograf that is revolutionizing the way the newspaper covers regional high school sports.

The Post is now able to give in-depth coverage of every high school football game played in the Washington, D.C., area each week thanks to Heliograf, which generates real-time updates and intelligent analysis based on structured data. As a result, the newspaper has been able to provide readers a more thorough and interesting sports coverage experience while simultaneously freeing up resources for other crucial responsibilities.

One instance of how NLG technology is changing how we consume and engage with news material is The Post’s usage of Heliograf in sports reporting. We may anticipate seeing ever more complex applications of NLG technology in the worlds of journalism and beyond as these tools continue to develop and get better.

3. A premier NLG technology vendor, Lingmill, has created a cutting-edge website that demonstrates the effectiveness of automated content generation in the field of sports reporting. The website covers all sports, from youth divisions to top professional championships, in great detail and is devoted to football and ice hockey in Sweden.

The unique feature of this website is that every article is created by Lingmill’s cutting-edge NLG text robot, which can produce interesting material in real time. The text robot is an indispensable tool for sports lovers and aficionados since it can give readers accurate and informative coverage of every game by utilizing structured data and sophisticated machine learning techniques.

The Lingmill website serves as a testimonial to the effectiveness of NLG technology in the realm of sports reporting and exemplifies its potential to fundamentally alter how we interact with and consume sports content. We may anticipate seeing even more amazing applications of this ground-breaking technology in the sports sector and elsewhere as NLG tools continue to develop and improve.

What difficulties does Content Automation using NLG face?

Although NLG technology has a lot of potential for content automation, there are still a number of issues that need to be solved before it can be effectively utilized. The following are some of the major obstacles to content automation with NLG:

1. Accessibility and caliber of data: Data availability and quality are two major obstacles to content automation using NLG. For automated content to produce accurate and trustworthy insights, it needs high-quality, organized data. As a result, content automation works best in industries like finance, sports, or weather, where data sources guarantee the accuracy and dependability of the information.

However, content automation can be more difficult in other situations when data is less accessible or of lesser quality. Before the data can be used efficiently in NLG systems, extra resources may be needed to gather and sanitize it.

Businesses and organizations must make investments in data collecting and management techniques that guarantee the reliability and accuracy of the data utilized in NLG systems in order to handle this difficulty. This can entail collaborating with external data sources or creating internal data collecting and management tools.

Overall, the success of content automation with NLG depends heavily on the quantity and quality of data. Businesses and organizations can make sure that their NLG systems produce precise, trustworthy, and valuable insights that drive value and enhance decision-making by investing in data collecting and management strategies.

2. Creativity and writing caliber: Maintaining originality and writing quality during content automation with NLG is another difficulty. NLG technology can only analyze the available data to provide answers to queries that have already been written. Algorithms are unable to pose new queries, identify needs, identify risks, resolve issues, or offer their opinions and interpretations on issues like social and policy change.

Auto-generated articles tend to be less creative than those that are authored by humans, despite the fact that machine learning and data augmentation approaches can enhance the quality of NLG content. This is due to the fact that NLG systems generate text using pre-existing data and templates, which might result in repetitive and formulaic content.

NLG systems must be created with creative and original elements in order to overcome this difficulty. This can be done through methods like template diversification, which allows for more variance in the text that is generated, or by employing neural networks to produce language that is more sophisticated and nuanced.

Additionally, in order to ensure that NLG-generated content satisfies strict requirements for writing quality, it must also be carefully examined and revised. The information produced by NLG systems may then be reviewed and improved using human editors.

In general, NLG systems struggle to retain originality and writing quality. Businesses and organizations can produce high-quality content that engages and informs their target audience by incorporating strategies for innovation and originality and guaranteeing high standards of review and editing.

3. Bias: Potential bias in the algorithms is a significant obstacle to content automation with NLG. Since NLG systems generate content based on facts and assumptions, the resulting material may also be prejudiced if the underlying data is inaccurate or biased.

This is a serious issue in the news and media, as slanted reporting can have long-lasting effects on the public’s perception and the democratic process. To overcome this difficulty, NLG systems must be created with methods for spotting and minimizing bias in the underlying data.

In order to allow for better accountability and scrutiny, NLG systems must also be transparent about the data and presumptions utilized in content generation. This can entail applying explainable AI methods that make it clear how the system arrived at its conclusions and suggestions for users.

Overall, minimizing bias in NLG systems is an ongoing problem that necessitates continuous monitoring and improvement. Businesses and organizations may create high-quality, impartial content that creates value and enhances decision-making by implementing methods for discovering and mitigating bias, encouraging transparency and accountability, and cultivating a culture of ethical and responsible AI use.

Check out our sortable, frequently updated list of NLG businesses if you have queries regarding NLG suppliers, or get in touch with us for additional details. Further information on this crucial subject, including bias categories, examples, best practices, and cutting-edge methods to decrease prejudice, may be found in our article on biases in AI systems.