What You Need to Know About Text Summarization NLP

The primary purpose of summarizing a text is to create…

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.

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Photo by Siora Photography on Unsplash

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.

Frequently asked questions

What are types of text summarization?

A text summarization approach is broadly divided into two types: extraction and summarizing. Abstractive Summarization.

What are various techniques for summarization?

  • An important summarization technique is selection.
  • Substitution is also an important summarization technique.
  • Rejection is an important summarization technique.

Why is text summarization difficult?

Since computers lack human knowledge and proficiency, automating text summarization can be a very difficult and non-trivial task. The task has been influenced by various models based on machine learning.

Why do we need automatic text summarization?

Automated text summarization methods are greatly needed to address the ever-increasing amount of text data available online to help both find relevant information and consume relevant information faster.

How do you approach text summarization?

The extractive summarization techniques vary, but they all share the same fundamental tasks: Construct an intermediate representation of the input text (text to be summarized); Score the sentences based on the constructed intermediate representation. Make a summary consisting of one of the top six most important sentences.

How is NLP useful for text categorization and text summarization?

Tagging text or categorizing text is the process of categorising text into groups or groups. Natural Language Processing (NLP) allows text classifiers to automatically analyze text and then assign a set of predefined tags or categories based on its content.

How does text summarization work in NLP?

Summarizing text based on extraction Using extractive text summarization, essential words are extracted from source material and combined to create a summarizing paragraph. Key bits of text are detected, cut out, then stitching them back together.

Is text summarization supervised or unsupervised?

As is customary, text summarization in NLP is treated as a supervised machine learning problem (where future outcomes are predicted based on received data).

What is text summarization in deep learning?

Use of the extraction of key words/phrases from the input sentence. Using the most important words in the input sentence for a summary, the idea is to create one.

What are the two main strategies used in text summarization?

Abstracting and extraction are the two broad categories of approaches to text summarization.

Which algorithm is best for text summarization?

  • Summarize Bot. Using this AI- and blockchain-powered tool, users learn more by reading less text.
  • TextSummarization
  • Text Compactor
  • SMMRY
  • Resoomer

What are the objectives of text summarization?

An abstract summarization system aims to identify the most important information from the text and present it to the end user.

What You Need to Know About Text Summarization NLP

Pam is an expert grammarian with years of experience teaching English, writing and ESL Grammar courses at the university level. She is enamored with all things language and fascinated with how we use words to shape our world.

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