How does text summary help concise long texts?

AI text summaries are the new face of writing.

But have you ever wondered how these text summaries work? What are the key principles on which your input texts are distilled into a few simple words? Let’s find out by reading this article.

Types of Text Summaries

Mainly, there are two types of AI text summaries: abstractive and extractive. Both of them work on sophisticated NLP algorithms to produce the desired results. To highlight the difference between the two, we will give a simple example for each type.

Abstractive

This type of summarization is more like a human approach to shortening text. The entire input is passed through a contextual window to produce a highly readable and understandable output: only the important elements are presented with unique words.

Extractive

Extractive summarization involves not altering sentences in the input text but rather extracting the most valuable ones for summarization.

Extractive summarization is best suited for tasks such as shortening a conversation between two people. Abstract summaries, on the other hand, are used for more general purposes, such as summarizing blogs, articles, research papers, and so on, or to effectively learn text summarization techniques.

How AI Text Summaries Work

Below, we have outlined in more detail the mechanism of operation of a abstract textual summary. We will also show you how it works in real life so you can use it to your advantage.

1. Text encoding

Text encoding is the first step in converting text into a format that the model can process. This is done using an encoder, specifically in a sequence-to-sequence (seq2seq) model, which is widely used in abstractive summarization.

The encoder transforms the entered text into a numerical form while maintaining its meaning. Since computers do not understand words directly, sentences must first be converted into weighted matrices .

Other models besides seq2seq can also encode computer text, such as Word2Vec, Bag of Words ( BoW ), and so on. Developers use different encoding techniques to ensure consistent results for abstractive AI text summaries.

2. Attention mechanism

To understand how the attention mechanism works, consider a simple example: when you wake up with a long list of tasks for the day, you can't do everything at once. Instead, you focus on the most important tasks first.

This is similar to how abstractive text summaries work. The model looks at the currently generated words and then selects the next word based on its relevance, considering weighted matrices.

As a result, the output you see is a condensed version of the original text that retains its main meaning.

3. Decoding

The decoder is the component responsible for finalizing the summary. It uses the encoded representation and the attention mechanism to generate the word-by-word summary.

The decoder starts with word tokens (packets) created by text encoders and attention mechanisms. It uses a Recurrent Neural Network (RNN) structure, typically a GRU or LSTM, to adjust the states of the hidden cells to produce the summary.

If the sequence of tokens is not clear, the decoder can use a strategy of ray search . This involves simultaneously exploring multiple token sequence paths to achieve a better result, although this method is resource-intensive.

4. Post-processing

After generating the summary, post-processing is applied to refine the output. This involves:

• Removing special tokens

• Truncation of remaining stop words

• Grammar correction

• Formatting

If a text summary includes bullet points, it may take longer to format. This is similar to the way AI text summary edit pad uses abstractive text summarization to create human-like output.

The tool accurately captured the essence of the text entered and created a bullet-point summary. Although steps 1-4 may seem long, tools like AI Text Summaries complete them in seconds. Their built-in NLP algorithms are well trained and optimized to serve business professionals, students, and content writers.

Final words

There are mainly two types of AI text summaries: abstractive and extractive. Both rely on NLP algorithms to shorten long texts for easier understanding.

Abstractive text summarization mimics human summarization by generating novel and concise content, while extractive text summarization preserves the original sentences. This paper discussed the details of an abstract AI text summarization and its working mechanism.

Abstractive text summaries use text encoding, attention mechanisms, decoding, and post-processing to quickly produce refined summaries. These summaries can be used by SEO experts, students, and anyone who wants to distill complex information efficiently.