Even though they may be perceived as similar, there are in fact huge differences between text summarization and text analysis. In a summary you try to extract the most relevant information from a text, obtaining as a result a shorter text that is concise, clear and as informative as the original text. You don’t add your opinions, point of views, insights from your domain knowledge, because all that you want is to allow the reader to gather a glimpse of what the text says without the necessity of reading the entirety of it.
On the contrary, when you perform text analysis, you do an entirely different operation: you add your thoughts! So in a text summarization you perforce try to discover and highlight connections within topics, to answer questions such as why and how, to build an argument and to reach conclusions.
Text summarize vs analyse: the main differences
Let’s see in further detail what summarize vs analyze truly means:
When doing text summarization you:
- Identify and clearly report the main points of a text
- Eliminate all redundancies or irrelevant information
- Report main thoughts or contributions by others
- Do not include your thoughts or opinions
When doing text analysis you:
- Make an argument and/or reach a conclusion
- Try to understand why each element is important and to enucleate connections between elements
- Identify and choose specific areas to elaborate on
- Could choose to discuss strengths and weaknesses, cause and effects, advantages or disadvantages
- Add your point of view
Of course both summarization and analysis are really important in business contexts: being able to do effective summaries may save you precious time and could be the only way to allow you or your employees to carry out your job. If you think that more or less 80% of all business data is text data, being able to move through it in an agile, quick way becomes imperative. Text summarization is the first step towards successful decisions because having all the information you need in a quick way allows your choices to be based on true knowledge of things and not on approximations or guesses.
On the contrary, thorough, detailed text analysis can give you precious insights, open new perspectives, change your points of views or confirm you in your positions, give you hints about the whys of what’s happening and so on how to react.
Unfortunately, roughly 2.5 billion GBs of data are created per day, most of which are text data (with an impressive 90% of the world data being created in the last two years) so no company could rely on just humans to scan, read, summarize and analyze this enormous quantity of texts (e.g. just think of all tweets concerning a famous brand, like Uber or Amazon).
So in today’s companies when we speak of summarize vs analyze text we speak of doing that automatically (i.e, by means of dedicated computer algorithms), at least in part.
For example, to have an overview of the latest text summarization techniques have a look at this article.
To automatically summarize vs analyze: use cases and business applications
Since text analysis and text summarization are so different, even when we consider their possible applications we have quite diverse scenarios.
Automatic text summarization could be extremely useful (also combined with a document datalization service) when you have lots of structured documents (e.g. scientific papers, contracts, business reports) and you or your employees do not have the time to read through all of them or want a completely neutral summary, not biased by opinions or previous knowledge. At PaperLit, a tech company of the Datrix group, we use the best available machine learning (ML), deep learning (DL) and natural language processing (NLP) techniques to perform and make available – even to small businesses – a state-of-the-art automatic summarization tool. By the way, it is to be noted that also spoken language can be transformed into text and then summarized, something that opens the door to an entire landscape of new possibilities.
Instead, automatic text analysis means digging deep into text to extract meaning and insights. How do you do that automatically? Here of course we speak of qualitative, unstructured data. While in the case of text summarization we dealt with reports, books, papers, articles, here we must handle things such as tweets, blog and social media posts, emails, support tickets, product reviews, and survey responses (a kind of data that can also be obtained by applying automated techniques, like web scraping). On this raw material we apply text classification techniques (i.e. we assign tags or categories to unstructured text based on its content) and on the results of this process we can then perform sentiment analysis, topic or intent detection, or apply further text mining techniques and then make the data you obtained easily readable using a data visualization tool, in thus transforming complex concepts into compelling and simple information.
In this way you could for example understand how your brand reputation evolves over time or compare it to that of your competitors, identify positive and negative aspects that boost or damage your reputation on social media, understand the reason why your business is growing or on the contrary is having a deflection, and many other things.
Also in this area the Datrix group, using the solutions provided by its company 3rdPlace, and ByTek, is able to deliver cutting edge services, which can really boost up your business. Contact us to have a personalized assessment of what we can do for your company.
