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Automatic Text Summarization: main applications

In the elementary school, most of us made many exercises to improve our ability to summarize a text. The summarization process is crucial, indeed, to understand the most relevant part of a text in an efficient way, to help our brain to memorize the text and to have a basis for linking different topics. Our digital world is full of content as never before and the necessity to summarize has never been so pressing. However, a human being cannot go that fast as new contents go. We need an automatic system to extract from the text the most relevant issue. This point is even more true if we imagine using a similar tool in a business context: we could improve the efficiency of a specific process and, in some cases, we could even imagine automatizing some simple tasks.

What is the starting point? A good automatic text summarization tool. In this article, we will deepen the theme and its main applications.

Automatic Text Summarization: state of the art 

Automatic text summarization can be defined as the process of shortening a text computationally, to create a summary that contains the most relevant parts of the original content. 

Obviously, the critical point is not to reduce the number of words or letters, but to train the machine to understand the grammar and the semantics of the text, in order to re-build its meaning and re-shape it in a reduced form. Nowadays, actually, automatic text summarization is considered as part of a broader subject, which is Natural Language Processing (NLP). As many of you know, Natural Language Processing is a subfield of Artificial Intelligence, which has the aim to train the machine to process the natural language and sometimes to re-use the natural language in order to answer questions (for example, we can think of chatbots or voice assistants).

There are two main techniques to summarize a text in a sensible way:

  • Extraction-based summarization: in this case, the algorithm extracts the key phrases and join them to create a new phrase. This technique is simpler than the following but, sometimes, the summary could be strange and incorrect from a grammatic point of view.

 

  • Abstraction-based summarization: using this technique, the algorithm creates new phrases, relaying the most useful information from the original text. It is easier to understand that this method is more similar to what humans do and abstraction, indeed, works better than extraction. Obviously, it is more difficult to develop a system based on abstraction.

 

Use cases and applications 

In the previous paragraph, we tried to make a quick overview about the techniques to summarize a text, now it is time to understand why you should use an automatic text summarization tool. So, let’s talk about the use cases and applications.

Automatic text summarization could be very useful for students or freelance. It is known that summarization is a key aspect of studying, for example. However, here we focus on applications in business contexts. We can identify at least three big categories: Customer Support; Internal Efficiency; Market Analysis.

Text summarization in Customer Support

The ability to summarize a text could be crucial to build a help desk system, both as personal assistant (ex. voice assistant, chatbot) and help documents. Large-scale summarization, indeed, could be the basis to collect all the answers for a question and, summarizing them, build a coherent and complete answer. 

Text summarization for Internal Efficiency

There are a huge number of automatic summarizing applications to improve the efficiency of different processes. Some examples are:

  • Better knowledge management, through the summarization of internal documents. The handling of two types of document could have a great benefit from automatic summarization tools: legal contracts and technical documentations. Nonetheless, it could be very useful scanning emails, as well.

 

  • Automated content creation: this is quite an innovative application, however companies should put really focus on it. Writing a good blog is just a matter of human creativity or machines can replicate, in some ways, the creation process of a text? This is a big question, but what is sure is that text summarization can play an important role.

 

  • Support the innovative process: the research and development processes, both in university and companies, have always a starting point, which is “What has been written on this topic so far?” The summarization could give an important help in this literature review part.

 

Text summarization for market analysis

What do your customer think of your brand? What are your competitors doing in terms of marketing actions or products development? These questions are not so easy to answer. Large companies usually have different teams focus on analyzing how the market is changing either in terms of product or customers’ desires and needs.

Using an automatic text summarization tool, you could understand in a more efficient way what is happening in the web, analyzing whitepapers, e-books and so on. You could even start from a transcription of a video and summarize it to extract the most important topics.

Brands and publishers: some hints to use a text summarization tool 

Manage and enrich the company digital properties is a big challenge, nonetheless this is one of the first application fields for an automatic text summarization tool. “Content is the king” – that is all marketers say nowadays. In this context, you need to set up a content care strategy in order to produce relevant, interesting and sometimes even educational content for your customers and prospects. An efficient process of text summarization could help brands to find new contents, to understand where the market is going and what the web is talking about. PaperLit (a Datrix Group tech company) uses this tool internally, to enhance the effectiveness of its content distributions. 

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