Extractive and Abstractive Text Summarization NLP

We have Extractive and Abstractive text summarization NLP. In NLP, Text Summarization models automatically shorten texts, articles, podcasts, movies, and more into their most essential soundbites. It is powered by sophisticated Deep Learning and Machine Learning research.

Developers are incorporating Text Summarization APIs into their innovative platforms to build call, interview, and legal document summarization solutions powered by artificial intelligence.

Here, we’ll define Text Summarization, go through its process, and highlight some of the top Text Summarization APIs.

What Is Text Summarization NLP Technique?

Text Summarization is a Natural Language Processing (NLP) technique. It uses Deep Learning and Machine Learning models to reduce significant texts into their fundamental elements.

Text Summarization can be applied to static texts like research papers, news articles, audio and video streams using Speech-to-Text APIs.

The summaries generated by different Text Summarization APIs vary in how they break down longer texts into manageable chunks of time.

Types of Text Summarization NLP

In this piece, we’ll look at some of the most common approaches to text summarization. There are two basic types of text summarization strategies: Extractive and Abstractive.

Let’s take a look at each approach, the steps involved, and how the result might differ based on the summarization method.

1. Extractive Text Summarization NLP

The conventional approach is called Extractive Text Summarization, and it entails the model “extracting” the most crucial sentences from the original text.

The text’s language is entirely preserved after using this method of summarization. Extractive Text Summarization condenses a longer text by extracting and isolating the most crucial information.

Methods for this include comparing how often particular words appear in the text. Assigning a numerical value to each word based on how frequently it appears in the text’s main body is another possibility.

One may then calculate the worth of each sentence by adding up the values of the words inside it. The remaining step is to sort the sentences by value and return the top-ranked ones. This is based on the assumption that connected sentences with familiar words provide summaries of all sentences.

2. Abstractive Text Summarization NLP

It’s no surprise that NLP’s fantastic growth over the previous decade has filtered into Machine Learning. In the middle of the 2010s, Abstractive Text Summarization NLP introduced the attention mechanism and enabled powerful new tools.

In most transformer-based methods for abstractive summarization, the input text is treated as a sequence and the output as another sequence.

GAN-based approaches to text summarization go beyond traditional transformer models. They teach a generator to produce summaries and a discriminator to distinguish between genuine and fake summaries.

Extractive and abstractive techniques are often combined in a single procedure. Text summarization is one of the most complex problems in natural language processing. Thus, researchers are always exploring new approaches.

5 Great APIs for Text Summarization

As a follow-up to our introduction to NLP and text summarization, we will now compare and contrast some well-known APIs for this purpose (APIs).

Some of these APIs do Text Summarization on top of audio or video stream transcriptions, while others only summarize the pre-existing text.

1. AssemblyAI’s Auto Chapters API

Highly accurate Speech-to-Text APIs and Audio Intelligence APIs are available from AssemblyAI. Its Auto’s Chapters API uses Text Summarization to generate paragraph-long summaries and sentence-long headlines for each chapter. This method is a novel implementation of AssemblyAI’s Text Summarization.

Top product teams use the API for various purposes, including podcasting, telephony, virtual meeting platforms, conversation intelligence platforms, and more. AssemblyAI’s Audio Intelligence APIs, including Auto Chapters, cost $0.000583 per second on top of the transcribing rate.

2. Plnia’s Text Summarization API

Summaries of documents and other pre-existing text bodies can be generated with the help of the plnia Text Summarization API.

Plnia provides additional services than only text summarization, including sentiment analysis, keyword extraction, a check for abusive language, and more.

Plnia offers a free 10-day trial for developers interested in trying the platform. It also provides plans incorporating Text Summarization beginning at just $19 per month.

3. Microsoft Azure’s Text Summarization

Azure’s Text Summarization API is a part of the service provider’s text Analytics package. It may be used to generate concise summaries of any document or article.

An Azure account and the Visual Studio integrated development environment is needed to start summarization. The API is a pay-as-you-go service, with fees varying based on volume and additional features.

4. MeaningCloud’s Automatic Summarization

With MeaningCloud’s Automatic Summarization API, users can quickly and easily extract the most essential lines from any document. They can also utilize them to create a synopsis that captures the essence of the original.

The API supports several languages, allowing its use regardless of the text’s original language. Anyone interested in trying out the API must create a free developer account.

After that, regular API users should expect to pay anywhere from $0 to $999+ each month.

5. NLP Cloud Summarization API

Text Summarization is one of the NLP APIs offered by NLP Cloud. It includes help for fine-tuning and deploying community AI models to increase accuracy.

Developers can create unique models, refine them through training, and then release them into production. Monthly costs can range from 0-499 USD, depending on your needs.

Final Words

Extractive and abstractive text summarization NLP is a process that can be performed within natural language processing using computer-assisted textual analysis tools.

As a field, text summarization is a critical tool used in the study of semantics. It is also becoming a more prominent area of research and application in the modern world.

The process of text summarization can be liberating in that it can allow direct access to raw data for more precise data processing.