Automatic content summarization refers to the technique of shortening long sections of text.
The aim is to create a coherent and fluid summary that contains only the points raised in the document. Therefore, automatic text synthesis is a common problem in machine learning and natural language processing.
The purpose is to replace the original text to encourage readers to read the content with clarity of thought.
We can distinguish two types of Summarizer :
-Informative summaries designed to replace the original text;
-Indicative abstracts, used as support, such as the title or abstract of an article.
According to extensive analysis of articles, reading frequency is closely related to text size. Often the abstract is the only item that needs to be read. Furthermore, writing abstracts is a fairly significant manual effort and incurs costs.
There are many use cases for automatic summarization, here are some examples. The most common is the simplification of reading.
In the school sector, this technology could facilitate e-learning. For example, a common case is using this method to help teachers design new subjects.
In an editorial context, we can find the writing of summaries of books, novels, or others. This task is still manual and difficult to achieve. By automating the summary and making it more qualitative, we will encourage the consumer to buy faster as he learns more about the topic ahead of time.
In science, there are many specific uses. A very relevant use is for key information in a report to quickly understand the idea of the text (medical, patient, financial markets, etc.). Example: List all of the patient’s medical history for the doctor. Practitioners will be able to care for patients with a global view.
If we refer to the current topic, we will find the automation of robots. Text automation is often a critical step in the process of optimizing chatbots (answers, searches, etc.).
Another area where the technology is not yet widely used is computer code optimization to help developers simplify or optimize their code to improve the performance of their applications.
Finally, for marketing, we can find the most common use cases: article summaries, content summaries or blog summaries to facilitate sharing on social networks to stimulate communication campaigns.
Generally, summarization automation is viewed as a supervised machine learning problem, i.e. predicting future outcomes based on given data.
Currently, there are two main ways to automate summarization:
-Extract text summarization methods work by identifying important phrases or excerpts in the text and copying them verbatim as part of the summary. Therefore, no new text is generated: only the existing text is used during the synthesis.
-Summary text summarization methods use more powerful natural language processing techniques to interpret text and generate new summary text, rather than selecting the most representative existing segments to perform synthesis.
Text summarization automation is still rarely used. However, this technique has many advantages for different users, regardless of their discipline, and can save valuable time compared to manual processing.
We are able to support clients in projects related to textual data and artificial intelligence. This approach is often used in the design of chatbots, where the company has strong expertise. This enables us to meet the needs of our customers on a business and technical level.
Automatic summarization involves analyzing one or more documents and generating a new, shorter document that allows users to access relevant information. The concept of relevance is clearly relevant to a specific context. Therefore, there are several types of abstracts.