Introduction
In primary school, the majority of us completed several activities to increase our capacity to summarise a book. The act of summarising is necessary for rapidly comprehending the most significant aspects of a document, aiding our brain in memorizing the information, and giving a basis for connecting themes.
Our digital world is more content-rich than ever, and the need to summarise has never been greater. However, a human being cannot keep up with the rate at which new content is created. We require an automated system to extract the most important issue from the text.
This is especially true when we consider using a similar tool in a business setting: we could increase the efficiency of a specific process and, in some cases, even automate some simple tasks.
What is the point of departure? An excellent text summarization program. We will delve deeper into the theme and its main applications in this article.
Automatic Text Summarization: state of the art
The process of computationally shortening a text to create a summary that contains the most relevant parts of the original content is known as automatic text summarization.
Obviously, the critical point is to train the machine to understand the grammar and semantics of the text in order to rebuild its meaning and re-shape it in a reduced form, not to reduce the number of words or letters.
Automatic text summarization is now considered to be part of a larger field known as Natural Language Processing (NLP).
Natural Language Processing is a subfield of Artificial Intelligence that aims to train machines to process natural language and sometimes re-use natural language to answer questions, as many of you are aware (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: The algorithm in this case extracts the key phrases and combines them to form a new phrase.
This method is less complicated than the following, but the summary may be strange and grammatically incorrect at times. - 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.
Applications of Automatic Text Summarization
The aim of Automatic Text Summarization (ATS) is to create a condensed version of a document. It retains the most relevant topics and material. ATS is not a modern area of research. Modern neural network-based methods capable of producing very impressive outcomes, on the one hand, and the availability of large-scale datasets consisting of hundreds of thousands of document-summary pairs, on the other, have reignited interest.
Furthermore, the ability to manage heterogeneous texts ranging from user-generated material derived from the internet to highly detailed documentation, such as technical/scientific articles, opens up new problems in this field of science. As a result, ATS is an important method not only for minimizing information material but also for assessing information validity. The appropriateness of answers in a given application context.
Research on summarization
This Research Focus aims to provide an overview of current research in the field of Natural Language Processing (NLP). In particular, ATS, in order to accelerate knowledge diffusion. This allows the creation of new methods, datasets, and services that meet the needs of research and industry.
To that end, the Research Topic encourages an interdisciplinary approach, with the aim of bringing together scholars with diverse backgrounds in a variety of disciplines — machine learning, natural language, cognitive science, and psychology — to explore cutting-edge work as well as potential directions in this promising area of ATS for multiple sources of data.
This topic’s priorities include (but are not limited to) discussing the following issues:
- summarization (abstractive and extractive)
- topic-based/query-based summarization supervised/unsupervised
- text summarization (single and multi-document)
- a variety of text genres (News, tweets, product reviews, meeting conversations, forums, lectures, student feedback, emails, medical records, books, research articles, etc)
- summarization of different languages and through multiple languages
- the development of new datasets and annotations, especially for languages other than English