What Is Text Summarization in NLP?

Introduction

 

Everyone in today's technology-driven society has to produce some kind of document online, whether it's a presentation, documentation, or even email. We often see that students need to produce large pdf files in front of their universities or colleges. 

 

The text contained in them sometimes becomes too lengthy and too hard to understand. Despite the immense resources accessible on the internet, getting through those large chunks of text can be highly perplexing for the user.

 

When natural language came into the market and we finally got something that can understand the language that we talk or write in, we felt relieved. One more reason for relief was the algorithms it had for natural human languages.

 

Natural language along with machine learning processing made it simpler to summarise lengthy amounts of text into a cohesive and fluent summary that only includes the document's important ideas. Although this was just a slice of the things that NLP did for technology this was one of the most helpful ones. 

 

“Text summarization”, yes, this NLP algorithm or technique has been a blessing for us. In this blog, we are going to look at what Text Summarization is and how it works.

 

Definition of Text Summarization

 

Text summarization is the practice of breaking down long publications into manageable paragraphs or sentences. The procedure extracts important information while also ensuring that the paragraph's sense is preserved. This shortens the time it takes to comprehend long materials like research articles while without omitting critical information.

 

The process of constructing a concise, cohesive, and fluent summary of a lengthier text document, which includes highlighting the text's important points, is known as text summarization.

 

Text summarising presents a number of issues, including text identification, interpretation, and summary generation, as well as analysis of the resulting summary. Identifying important phrases in the document and exploiting them to uncover relevant information to add in the summary are critical jobs in extraction-based summarising.

 

To have a better understanding of text summarization and automated text summarization, watch this: 

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Requirement of text summarization

 

The amount of text data available from various sources has exploded in the big data age. This large volume of text has a wealth of information and expertise that must be adequately summarised to be useful. 

 

Because of the growing availability of documents, much research in the field of natural language processing (NLP) for automatic text summarization is required. The job of creating a succinct and fluent summary without the assistance of a person while keeping the sense of the original text material is known as automatic text summarization.

 

It's difficult since, in order to summarise a piece of literature, we normally read it completely to have a better knowledge of it and then write a summary stressing its important points. 

 

Because computers lack human language and understanding, automated text summarization is a complex and time-consuming operation. Furthermore, using text summarization decreases reading time, speeds up the research process, and expands the quantity of information that may fit in a given space.

 

(Related reading: Text Cleaning & Preprocessing in NLP)

 

Different types of Text summarization

 

Text Summarization is classified on different bases. They are:

 

• On the basis of Outcome

 

There are mainly two types of text summarization in NLP:

 

1. Extraction based summarization

 

The extractive text summarising approach entails extracting essential words from a source material and combining them to create a summary. 

 

Without making any modifications to the texts, the extraction is done according to the given measure. The diagram below will make us understand extractive summarization better:

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Extraction based on summarization, (Source)

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This approach works by detecting key chunks of the text, cutting them out, then stitching them back together to create a shortened form. As a result, they rely only on phrase extraction from the original text. 

 

2. Abstractive Summarization

 

Another way of text summarization is abstractive summarization. We create new sentences from the original content in this step. 

 

This is in contrast to our previous extractive technique, in which we only utilized the phrases that were present. It's possible that the phrases formed by abstractive summarization aren't present in the original text. 

 

When abstraction is used for text summarization in deep learning issues, it can overcome the extractive method's grammatical errors.

 

Abstraction is more efficient than extraction. The text summarising algorithms necessary for abstraction, on the other hand, are more complex to build, which is why extraction is still widely used. (here)

 

• On the basis of Context

 

3. Domain-Specific

 

In domain-specific summarization, domain knowledge is applied. Specific context, knowledge, and language can be merged using domain-specific summarizers. For example, models can be combined with the terminology used in medical science so that they can better grasp and summarise scientific texts.

 

4. Query-based

 

Query-based summaries are primarily concerned with natural language questions. This is similar to the search results on Google.

 

When we type questions into Google's search field, it occasionally returns websites or articles that answer our questions. It displays a snippet or summary of an article that is relevant to the query we entered.

 

5. Generic

 

Generic summarizers, unlike domain-specific or query-based summarizers, are not programmed to make any assumptions. The content from the source document is simply condensed or summarised.

 

 

Working of text summarization algorithm

 

Text summarization is typically approached as a supervised machine learning issue in NLP. Here, we'll look at how text summarization techniques function, as well as several machine learning models.

 

This is how the approach should be taken. 

 

• Create a method for extracting the important keys from the original document. 

• Collect text documents with keywords that are favorably labeled. The keys must be compatible with the extraction method specified. One may also build negatively labeled keys to improve accuracy. 

• To produce the text summary, train a binary machine learning classifier. Finally, in the test phrase, generate all of the relevant words and phrases and classify them accordingly.

 

Sequence-to-Sequence Modeling (Seq2Seq)

 

We may use a Seq2Seq model to solve any problem involving sequential data. Sentiment categorization, Neural Machine Translation, and Named Entity Recognition are examples of popular sequential information applications.

• The input is a text in one language, and the output is also a text in another language in the case of Neural Machine Translation.

• The input for Named Entity Recognition is a list of words, and the output is a list of tags for each of the words in the list.

 

The two major components of Seq2Seq modeling are Encoder and Decoder. Let us understand the concept of these:

 

1. Encoder

 

The complete input sequence is read by an encoder Long Short Term Memory model (LSTM), with one word being sent into the encoder at each timestep. The information is then processed at each timestep, and the contextual information existing in the input sequence is captured.

 

2. Decoder

 

The decoder is likewise an LSTM network that analyses the whole target sequence word-by-word and predicts a sequence that is one timestep delayed. Given the previous word, the decoder is trained to anticipate the next word in the sequence.

 

(Also read: Textless NLP: Definition & Benefits)

 

Now that we know how Text summarization works, let us end the series of information with advantages of the same.

 

Advantages of Text Summarization

 

• Instantly effective

 

It takes time and effort to read the entire article, deconstruct it, and separate the significant concepts from the raw text. It takes at least 15 minutes to read a 500-word article. 

 

In a fraction of a second, automatic summary software summarises texts of 500-5000 words. This enables the user to read less data while still getting the most critical information and drawing sound judgments.

 

• It Functions in Any Language

 

Many summarization software can work in any language, which is a capability that most humans lack. 

 

Because summarizers are based on linguistic models, they can automatically summarise texts in a wide range of languages, from English to Russian. As a result, they're great for persons who read and work with multilingual information.

 

• Productivity is increased

 

Not only do some software summarise documents, but they also summarise web pages. This boosts productivity by accelerating the surfing process. 

 

Instead of reading entire news stories that are full of irrelevant information, summaries of such websites can be detailed and accurate while yet being only 20% of the original article's size.

 

(Must read: Types of Machine Translation in NLP)