The primary purpose of summarizing a text is to create information that is easy to understand and conveys the main parts of the document. Text summarization NLP automatically extracts the main points in a text and presents them in a short form while maintaining the original document’s readability.
What is Text Summarization?
Text summarization involves extracting the key concepts within a text and putting them into a single form. The goal of text summarization is to create a concise and consumable article which ultimately achieves the most specific purpose of the original text. A summary must be as compelling as the original document itself.
Text summarization is to produce a new text that is an abridged version of an existing text. It is helpful in using more significant amounts of unstructured and language data to extract or generate meaningful information. Text summarization works by extracting essential information, such as the highlights of a news article.
Text Summarization NLP
Natural Language Processing (NLP) describes the field of using computer and natural language to improve the ability of computers to interact with humans.
In practice, NLP involves using computers to perform a variety of tasks, such as text summarization, parsing, entity recognition, and speech recognition. One of the most popular ways NLP accomplishes text summarization is by extracting the main paragraphs from a text.
In NLP, text summarization is the process by which artificial intelligence or a machine-learning algorithm is inferred from text. The analysis of summarization can yield the insight that the text is about a particular topic or thing.
Unlike deep learning, text summarization is machine-based, which is appropriate, given the short-text processing timeframes required to generate outputs.
Text summarizing NLP is a core process for summarizing text content. It involves emphasizing the main features and presenting them in a single piece of text.
Approaches to Text Summarization
Text summarization entails taking a piece of text, and reducing its size by summarizing the essential details into a short length of text. The goal of summarization is to extract key points from the text and make it easily digestible and comprehensible.
Generally, the primary approaches to text summarization are the extractive and abstractive NLP. Other approaches to text summarization are linear and statistical summarization. These are considerably less well-known but can be implemented through computer algorithms.
The primary approaches to text summarization are discussed below.
1. Extractive method
A subset of summarization, the extractive method is the task of selecting and extracting text from documents to create summaries. The extractive method only has one objective, to extract the most essential pieces of information contained in the document.
In the context of document summarization, extraction is selecting from the text, a subset of information that should be included in the summary. This most often involves the selection of sentences that can be quoted or paraphrased.
The summary obtained through the extractive method contains exact sentences from the original text.
2. Abstractive method
The abstractive method is another subset of summarization that involves generating a summary of the whole document at once.
Abstractive models use advanced NLP to understand the semantics of text and generate significant summaries. This approach involves identifying the most critical sections, interpreting the context, and replicating them in a new way.
This means that the essential information is conveyed at the shortest possible length. In this case, the summary sentences are generated, not extracted from the original text.
To Wrap Up
The goal of text summarization is to create a concise and consumable article that conveys a document’s most important aspects. Text summarization NLP is a way for computers to take a block of text and create their representation of it. This article has provided you with a general overview of text summarization NLP.
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