Cutting Through Nonsense in Large Text Documents

Sifting through mountains of irrelevant text just to locate important nuggets of information is as irritating as it is inefficient.

Yet, whether you’re a master’s student putting together your thesis or an engineer trying to categorize technical specifications, you’re going to have to do just that. Worst of all, these kinds of texts run rampant with ambiguities and unclear language making it unusable. Furthermore, they aren’t usually formatted with navigational purposes in mind.

Most of the time, extracting the important information out of text-dense documents is time sensitive, but how can 500 pages be analyzed for specific data in good time?

Improve Your Manual Process


Techniques such as speed reading can aid you tackles these large documents, but there are some faster, out of the box methods, that you can try out and which can help you tackle all that data.

If you’re a lawyer, engineer, economist, or any other highly-educated professional, you’ve had to make your way through undergraduate and graduate programs. One of the main principles of studying and learning is knowing what not to read when trudging through arduous texts such as scholarly and academic journals.

That principle never changes once you’re out of the realms of academia, and since every industry has deadlines you can’t waste time absorbing superfluous information.



Here are some tips on how to get to the meat of larger documents:


1) If a document contains an abstract, read the “findings” and “conclusions” sections first as they usually contain the pertinent information

2) Read the first and last sentence of every paragraph in order to get the gist

3) See if you can find a better-written source with similar information, it may make it easier to work through the more complex text

3) Understand the style format of text you’re reading so you know what to avoid




By mastering these more traditional methods of gathering necessary information, you will be well on your way to better efficiency but harnessing modern technologies can push you the extra mile.

Technology to Rescue


Modern advances in technologies, such as natural language processing, have given rise to a number of intelligent tools to help in different situations. Whether it be thousands of technical specifications, a large pile of CV’s or an uncontrollable amount of customer feedback, we can find tools that can help process the data and give extra insight.



Finding The Right Information In R&D



Collecting a reading list for a new problem takes industrial researchers an average of 3 weeks, with some of the material still being irrelevant for the project. Using artificial intelligence software, these mundane tasks can be sped up by using solutions such as Iris.ai’s exploration tool. Iris.ai builds an interdisciplinary research map based on a problem statement or research paper of your choice, and delivers a precise reading list in less than 2 days.

Accessing Emotional Insights

Many retailers need to understand what’s being said about them on social media. For those who’ve found themselves through various Twitter or Facebook wormholes, social media can present a vacuous hole of worthless text.

Even still, if you don’t assess endless social media posts about your products or services, you won’t be able to optimize your response to issues or fully grasp how your company is performing.



Companies such as Lumoa allow you to track all your customer experience insights from an online platform; finding what drives the customer experience, comparing it with KPI’s and understanding what you are doing well, and what you should be doing better.

A more do-it-yourself solution is also available from Amazon. Amazon Comprehend uses sentiment analysis to computationally determine whether a piece of writing is positive, negative, neutral, or mixed. It can be used as part of a serverless event driven architecture on Amazon Web Services.

Engineering Document Analysis

On the engineering side, tools such as Selko Analytics extract vital information from text-dense engineering specifications. Engineering departments can easily set up their own machine learning model through the tool and pre-process technical text to spot contractual risks, streamline procurement activities, and categorize tasks by groups or architectural levels.



The industry standards demand an influx of data in need of processing, analysis, and implementation, particularly given the complexities of safety and engineering requirements. Time is often of the essence and the engineering specifications are large written documents which need to be processed to get new projects underway.

Selko Analytics’s utilises its intelligent search features to locate items of text adhering to certain groups. Using its user-trained machine learning technology one can properly categorize text after analysing only 100 samples.

Requirements and regulations can be assessed with more efficiency and detail. All results can be verified by a person before export, and any changes made will be learned to produce better results next time.

Solutions Are Out There


Locating the important information in large documents with impenetrable walls of text is frustrating for any professional with a need to get projects and initiatives off the ground.

Thankfully there are plenty of methods to help you through this difficult process. Manual “scanning” methods employed by students are good tips that help with large documentation, but modern technology tools can also be utilised for improved efficiency and productivity.

Text based data can be hard to keep consistent and to navigate, and walls of pointless text can seem frustrating, but there are plenty of solutions abound to cut through the jargon and get right to the point.