Category: Publishing Automation

In words of G.K.Chesterton “, The only way of catching a train I have ever discovered is to miss the train before.” In this first blog of our Modernization series, we discuss compelling reasons to convert legacy Flash content as soon as possible.

This is what the Adobe website shows at present:

“Since Adobe no longer supports Flash Player after 31 December 2020 and blocked Flash content from running in Flash Player beginning 12 January 2021, Adobe strongly recommends all users immediately uninstall Flash Player to help protect their systems.”

It’s almost a decade since Steve Jobs in his open letter outlined reasons why Flash would not be allowed on Apple products. Undoubtedly, this might have triggered a steep descent for Flash. At the same time, we saw the sudden rise of HTML5, which became popular as it is easily customizable, browser friendly, and supports multimedia integrations.

While most organisations have already migrated Flash courses to HTML, there are still some who could not do, so mainly due to budget constraints or other pressing priorities. However, with every passing moment, it is becoming inevitable that both quality training and competitive advantage are at stake.

Here are five top reasons to convert Flash courses:

1. Security

There have been security concerns with Flash for many years now, making it vulnerable to cyber-attacks, hacking, and malwares. Because Flash has reached End-of-Life (EOL), no new security patches or updates will be available. There’s no surprise why browsers like Google Chrome, Mozilla, and Safari have stopped supporting Flash content.

2. Suitable content for modern-day learners

With attention spans shrinking to just few seconds, a modern-day learner relies heavily on mobiles and tablets to learn, bite-sized yet meaningful content. Unfortunately , Flash-based courses won’t run on mobile devices. Now, imagine you have a library of Flash courses that you would like the modern-day learner to complete. It simply won’t work, and more importantly, the whole purpose of delivering quality training would be lost. HTML5 has emerged as highly preferred technology that helps to view courses on all devices and across all browsers. 

3. Alignment with strategic business goals

In the post-pandemic world, online training has gained attention like never before. Most organizations have started to budget/invest in quality online training to suit modern-day learners’ needs. From an organisation’s perspective, the strategic business goal would be to improve the bottom line by expecting employees to perform at their absolute best.  Needless to say, to achieve this goal, employees need to get access to the latest and greatest learning content. There is absolutely no room for legacy Flash content, as it would simply not align with strategic business goals. 

4. Browser support

Since Adobe is no longer supporting Flash, they are blocking and disabling all Flash content from web browsers, including Chrome, beginning January 12, 2021. Now, what does this mean for employees who try accessing Flash-based content? It could be very frustrating, right? On the other hand, HTML is much more flexible and works across all browsers. Additionally, HTML5 is SEO friendly and does not need any plug-ins, unlike Flash.

5. Future-proof

As Flash was a proprietary tool, it was very cumbersome to make new updates ; hence, it became difficult to match the pace of changing technologies. Since organisations invest heavily in developing e-Learning courses, they would prefer to select a technology that is self-updating, keeps up with new learning technologies, and most importantly is Future-proof. These factors would eliminate the need ot re-invent the wheel and migrate to new technology each time.

To summarize, these five reasons should encourage you to convert any remaining legacy Flash content . For organisations, it could be a great opportunity to provide best-in-class learning content to employees. And for catalog companies, it could be a new revenue stream altogether.

Publishing has witnessed some very interesting and disruptive transformations over the years. In today’s digital landscape, the publishing industry is quickly moving from a book-based product model to a services-based business model. Moreover, technological advancements have brought with them multitudes of opportunities and possibilities in learning and publishing.

Technological advancement isn’t complete without bringing in the term Artificial Intelligence (AI). Before we continue, let us quickly break AI down for you:
AI helps in building systems that can carry out intelligent tasks. Machine Learning (ML) and Natural Language Processing (NLP) are the two subsets of AI. While ML helps in building systems that can learn from experience, NLP helps in building systems that can understand language. Used together, ML and NLP help in building systems that can swiftly pick up languages.

AI capabilities have advanced to a large extent. In the context of the Publishing space, AI can automate significant portions of workflows, implying a direct positive effect on businesses and authors as well as the research community.

It is a long road ahead for NLP and AI to emulate human intelligence in content creation, and it is to be seen how both the industry and consumers together respond to a prospect like this. However, it cannot be ignored that NLP has made its indelible impact in the Publishing industry by incorporating grammar analysis into computer programs to recognize parts of a sentence and understand the structure of words to discern their meaning. NLP finds itself useful in the Digital Publishing space in various capacities:

• Generating Summaries and Synopses: Publishers only have a moment to grab the attention of readers. With diminishing attention spans, short reviews or synopses could help the reader decide on whether to continue or not, especially in the case of journals, chapters, research papers, academics, or stories. NLP can help publishers quickly and easily generate accurate summaries of text from longer content pieces.
• Customer Service: ML and NLP alongside text analysis and computational linguistics can help develop algorithms to provide the right answers to customers almost immediately. Through sentiment analysis, these can help decipher the customer’s mood and react accordingly, thereby steadily improving customer service.
• Social Media Management: While ML can be leveraged to identify the right target audience on the social media platform, personalise content, and choose the timing for posting, NLP is used to understand and analyse social behaviour. The combination of both helps optimize social media channels and the critical task of managing reputation.
  • Automating Processes: Publishing involves various stakeholders, with publishers, editors, production, legal, developers, and marketing, each bringing their own internal workflows and processes. Automating these processes can reduce time to market, and NLP comes to the rescue here.

NLP in Editorial Management

The past witnessed NLP being employed only in analysing articles to determine the subject and define the metadata keywords for the article. Today, NLP finds its applications in Publishing and Editorial Management in many ways, going beyond mere copyediting.

• Assessing Content Quality: NLP can help gauge the quality of content in terms of grammar as well as formatting. Any software that employs NLP will be able to read the manuscript, understand it, and determine if it’s well written. This, in turn, helps the author communicate in the best way possible.
• Simple Editing and Formatting: With NLP, it is also possible to auto-fix some simple grammar and punctuation mistakes and flag more complex issues that may need an editor’s attention. Publishers can thus automate simple editing and formatting tasks and invest their energy into adding greater value to the content.

Benefits of NLP

• Avoids Multiple Iterations: Copyediting, formatting, composition, and proofreading are important steps in Publishing and cannot be ignored. But these may result in the manuscript going back and forth between the author and editor many times before it is appropriate for publication. This entire process could take several weeks. AI and NLP come to the rescue here by reducing the number of iterations in the process.
• Speeds Up Processes: AI and NLP work their magic in fast-tracking processes. Publishers can handle more journal submissions and conduct faster peer reviews without increasing staff or production costs.
• Provides Better Author Experience: The reduced time taken to publish an article will likely incentivize more authors to submit articles to its journals.
• Helps Deliver Relevant Content: The faster that new content can be published, the better its relevance. NLP also enables Publishers to deliver important research and ideas to the scholarly community faster.

With more innovative ideas to use these technologies, AI and NLP will slowly improve every stage of production, resulting in near instantaneous publishing. These technologies could help analyse a database of scholars with varied expertise and match the best individual for an appropriate peer review. AI and NLP can also identify when sections of articles have been taken directly from another source or where citations are missing or incorrect, thereby resolving issues of plagiarism. AI and NLP will not replace editors, but they will empower editors to provide more value to their authors.

With over a decade’s experience in the publishing domain, Integra today is one of the few end-to-end solutions providers for publishers. Find out how we use NLP to help our Publishers fast-track their processes. Speak to one of our experts today here.

Artificial Intelligence (AI) has been pervading the digital publishing space quite effectively of late, easing out labor-intensive processes and streamlining several services across the spectrum, Natural Language Processing (NLP) is yet another interesting phenomenon that has gripped the attention of those in the learning and digital publishing arena.

With such innovation taking place at a rapid pace, what’s in store for digital publishers and their audiences?

Aiming for precision and context-sensitivity

One of the primary issues with several ‘keyword-based’ analysis systems is that they lack precision. They often count words as ‘character strings’ so that analysis runs faster, but doing so removes crucial contextual understanding and insight, making word matching difficult. It is here that NLP can add value owing to its differentiated offering.

With semantic cognition at its core, NLP understands each word on a page as a human brain does. This allows it to extract the true meaning, resulting in more insightful and precise classifications.

Replicating human understanding to create better experiences

We humans typically use all our senses to code experiences internally. The technical term for this is `internal representation’, given that the word `imagery’ does not immediately conjure up the role of hearing, feeling, tasting, smelling, and movement in the coding of experience.

NLP cognitive solutions have the ability to replicate human understanding and read content the way humans do, owing to semantic technology that can make sense of subtle changes in the meaning of words, specific to their context.

Creating highly personalized content

NLP aids granular data analysis. This is at the intersection of other data types, such as demographic, psychographic, or behavioral data, and can drive insightful understanding of user preferences. This is an important step towards enabling the creation of tailor-made content that suits distinct user segments.

Reorganizing data to understand the audience better

Advanced cognitive technologies such as NLP not only enable publishers to turn silos of data into orderly profiles, but also analyze responses to digital learning content and predict the kind of content that works for them, both in the present and also for the future. Equipped with this data, publishers will have the means to significantly meet user demands at a rapid pace and continue staying attuned to their growing needs.

The bottom line

For the average consumer, AI and its associated technologies such as NLP might mean more automation and, in effect, more time on their hands. For publishers, however, it promises a whole new world of possibilities. From gaining perspective through a unique understanding of audiences to optimizing content, publishers can save both cost and time.

What’s more; NLP is set to do a lot more.

Powered by NLP and AI, iAuthor is an intuitive, collaborative content authoring and editing platform that enables publishers to achieve rapid publication cycles – from manuscript processing to delivering print-ready PDFs or multiple e-formats ready for distribution.

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Within digital publishing circles today — when you have terms like: Artificial Intelligence (AI) and Natural Language Processing (NLP) thrown around ever so often, you would do yourself some good to get acquainted with them and in fact get around to exploring them in real-time for potential use cases in the publishing space.

With complex workflows characterizing a big part of the publishing ecosystem, AI capabilities are quite promising to the extent that they can actually automate large portions of these workflows, and create significant value to publishing businesses, authors, and the research community.

With this blog, let’s decode how AI and NLP render themselves to value in the publishing space.

Empowering editors with better decision-making

AI augments human ability by helping machines identify and create new patterns. It involves developing algorithms that enable machines to quickly process large scores of data, recognize consistent patterns within that data, make insight-driven decisions, and provide recommendations based on that analysis. In essence, AI can simplify the publishing process to the extremes.

Correcting minor errors

With built-in AI and NLP technology, digital editing tools can analyze each article that comes their way with pre-set grammar and formatting rules. After analyzing the content, the technology helps evaluate whether or not the article is of good, publishable quality. The technology will automatically correct minor errors like grammar and punctuation, and flag more complex issues that may need an editor’s attention. Journal submissions that are high-quality and can advance straight to the typesetting and composition stage.

Automating workflows

AI can intelligently automate workflows in order to reduce time to publication, improve editorial quality, enhance author experience, and boost the immediacy of science. AI and NLP can be leveraged to create a splash in the peer review process by evaluating manuscripts, extracting key terms for originality and relationship mapping, checking for language quality, identifying plagiarism, and even matching papers to journals and reviewers.

Accelerating internal production processes and reducing lead times

Since production processes are very time-consuming and there is a constant demand for faster, cheaper, and shorter turnaround times, AI can  increase the speed of the editing process and better serve its authors with more timely publication. From sifting through the large volume of incoming content to flagging content and process anomalies, AI/NLP has become indispensable.

NLP incorporates grammar analysis into machine-learning. A computer program is trained to recognize the noun, verb, and object in a sentence, and to understand the structure of words in order to discern their meaning. With NLP technology, publishers can automate simple editing and formatting tasks and focus on more complex tasks at hand.

Reducing lengthy review cycles

By reducing the number of touchpoints that a document has to flow through, AI and NLP together can increase the speed through the system, using algorithms, rather than having to wait for manual review cycles that are typically long and time-consuming. This cuts down on manual editorial intervention and could possibly even reduce the element of human bias.

So, what’s the final take?

There are two ways of seeing the benefits that AI/NLP can bring to the publishing table. On one hand, they can be used to streamline publishing workflows and thereby improve author experience since data-driven decisions always take the cake over off-hand conclusions. On the other hand, publishers see AI and NLP as useful technological tools that can drastically reduce costs and drive operational excellence, all along improving publishing workflow efficiency and output quality.

The decade that went by has witnessed the spectacular rise of digital publishing, print-on-demand, and the independent author movement. To feed this explosive growth, several technologies and tools have been gaining traction. Artificial Intelligence (AI) is particularly promising in that it looks to be the next big disruptor in the publishing field.

Let’s take a closer look at how AI is going to create a strong impact on the publishing industry:

1. Reading into the Future: Predictive Analytics

By using complex algorithms and rigorous methods of current and historical data analysis to make predictions on the future of readers’ behavior, AI can automate data forecasts to a significant extent. By automating data forecasts, publishers can redefine their monetizing strategy in the most effective way. The New York Times, for instance, has had a successful foray in this regard by employing data science and machine learning (ML) to increase subscription-based revenue. OptiQly, a marketing technology and services startup, uses AI-powered tools to increase book discovery and sales at online retailers.

2. Doling out the Right Stuff: Content Personalization

This has long been discussed and is viewed as one of the most useful and sustainable benefits of AI for publishing. An AI-backed personalization via email makes it possible. How it works is simple: While a user interacts with digital content via a learning services portal or accesses content through a reading platform, an intelligent algorithm learns one’s behavior, defines preferences, and identifies pages and topics with the highest engagement rate. It then compiles a list of the most relevant content links with auto summaries to send out as a newsletter, exactly when the user is most likely to open and click through to read the content on the website.

3. Picking the Picture: Image Recognition and Auto-tagging

The publishing industry has long been seeking an optimal solution for image storage that would provide a faster and enhanced file search. As an extension to that, yet another challenge is manual image tagging. This requires a human to invest several hours of mundane work, introducing an element of human error. Given that image archives are hard to navigate, AI-powered tools may seem promising in this regard, especially when thought through in close conjunction with human labor.

4. Passing the Verdict: Content Evaluation

The manuscript approval process can be quite a stressful activity for an editor since it calls for a huge investment of time and effort from the editor’s end. With AI in the picture, the possibility of comparing an unpublished piece of work with a group of bestsellers of the publisher in the same genre is presented. This helps to identify their shared traits and evaluate whether or not the new book has the potential to engage readers.

5. Dealing with Management: Content Organization

AI can also help with efficiently managing and organizing content, which has traditionally been a serious issue due to the unstructured nature of video and audio data. All the recent advances in speech and emotion recognition, as well as computer vision, have empowered the most recent AI tools, which can now easily classify archives that were previously thought inaccessible. Not just that, AI brings to the learning services and education table a much-needed structure and orderliness. This is beyond vanilla content, rendering itself to a rather comprehensive learning experience.

All said and done, AI cannot – not now, nor in any currently foreseeable way – ever replace the real purpose of publishing or the human aspect of the business, which is to transmit ideas, stories, wisdom, and inspiration between humans. To a great extent, AI looks as if it can be a force that can augment human wisdom and knowledge.