Automatic text summarization is a field that is experiencing much interest and research lately because of its applications(automatically summarizing research papers, long reports, entire books, web pages, news, etc).
There are two ways of attempting an automatic text summarization: The extractive is the easiest one, consisting of a “collage” of sentences extracted from the original document; the abstractive approach on the contrary focuses on obtaining a shorter version of the original text using different words and sentences, rephrasing the text in the same way as a human would do.
It is a difficult task because it implies a sort of semantic “understanding” of the text, something which it’s really borderline with strong AI objectives and that researchers have not yet fully obtained.
Deep learning for automatic summarization
Deep learning methods proved to be particularly promising for this specific purpose since they attempt to imitate the way the human brain operates by managing several levels of abstraction and nonlinearly translating a given input into a given output (in this process the output of one layer becomes the input of the other layer and so on). Clearly, the greater the number of layers, the greater the depth. Deep neural networks are commonly employed in NLP difficulties because of the fact that their design works well with the complex structure of language; for instance, each layer can handle a separate job before passing the result to the following layer.
Typically, when one thinks about summarising, articles, reports, and scientific papers come to mind, texts that are not excessively long. Occasionally, however, this is not the case, and you must deal with a variety of text types: studies, detailed reports, and even complete novels.
In this situation, you must employ a different technique and decide when to apply traditional or machine-assisted summarization. Also, take in mind that summarising and paraphrasing are two quite different activities (state something using different words). In the event of lengthy texts, you should also be able to choose when to summarise and when to paraphrase.
Long texts: paraphrasing or summarizing?
When you paraphrase, you use alternative words to convey the same meaning as the original text. You choose to paraphrase when the meaning of the text is equally as essential as its words, when you wish to communicate the same idea using simpler, more direct language, or when you wish to put the text within a lengthier text written in a different manner.
Obviously, the purpose of paraphrasing is not to reduce the text, but rather to express the same meaning using alternative words. Therefore, you cannot use it when synthesis is essential; instead, use summarise.
The summary gives a concise synopsis of a text, a reduced version of it that retains all essential details. When dealing with long documents such as novels, it is evident that you must select to summarise. Consider the process of summarising lengthy texts and novels.
Summarizing long texts: choosing a strategy
The main difficulty when you summarize long texts or books is organizing the information in an effective way. You may choose different strategies:
- Read the entire book or text, take notes while you do it and then try to sum it up in only one step writing a text that condenses the book’s contents in your own words
- Read chapter by chapter or paragraph by paragraph and do mini summaries of each one. Then use this material to compose a general summary
- Read the book quickly to gather the most important idea or ideas the book is around to. Then draw a conceptual map for the various sections or chapters to show how they connect to the main idea. Use this material to put together your summary.
Remember that you are not required to follow the same logical or chronological order of the book: you can begin with the most important topic or idea and then go into more detail, or you can group topics, or you can follow any order (chronological, narrative, logical) that allows you to summarise the book or text without losing relevant information.
Also, keep in mind that a summary is not a review: NEVER include your thoughts, personal beliefs, or arguments in a summary. A summary should be objective and neutral.
Benefits of summarizing
But you could ask: is this work worth the effort? Why bother?
The reason why summarizing could be extremely useful for you and your business is several:
- It allows us to go into the text’s subject matter and to fix ideas and concepts
- It helps you organize and clarify your ideas about the book and to structure your thinking
- If you sum up books or texts that others are interested in it could build an audience or readers. It helps you in building connections and expand your visibility
If you need to go fast…
As you would guess, summarising a lengthy paragraph or, worse, an entire book is a laborious and time-consuming endeavor. It demands focus and great exertion. If you must summarise many books, the problem becomes even more complex. In such a circumstance, you can utilize technology for assistance. Specifically, current advancements in natural language processing and machine learning.
In recent years, great progress has been made in the field of study devoted to automatic summarization. From merely being able to comprehend brief texts, it is now capable of handling increasingly lengthy pieces of literature, even whole novels.
There are several algorithms based on various methodologies; for summarising lengthy texts, the ones with the best performance are currently those based on the extractive approach.