Summarization in NLP

Text summarization is a shortcut to reading an enormous set of documents. For instance, instead of reading a complete article or research paper, you might just read the most important statements; this is done by text summarization.

Today, text summarization's scope is not just limited to documents, it has a wide range of applications across various domains, such as news summarization, social media summarization, and email summarization.

Let's explore the fundamental types of text summarization.

Based on input

The input decides what type of text summarization is to be performed. The input may be:

Single document: A single document or a smaller text size is processed to get the important points from it.
Multi-document: Multiple documents or a larger text is to be summarized.

Based on output

The output decides what type of text summarization is to be performed, the output may be aimed to be abstractive or extractive.

Extractive summarization: This summarization approach focuses mainly on identifying the important phrases for input text and combining them together to generate a summary. Most text summarizers today are extractive.
Abstractive summarization: It is an approach similar to that of a human. It goes beyond extracting the important sentences and instead gains the core meaning of the text and understands it, and only then generates a summary.

Based on purpose

In some use cases, the text summarizer technique is used based on the purpose to be fulfilled. The purpose may be generic, domain-specific, or to answer a query from the input text.

Generic: In this approach, the model makes no assumptions about the domain or topic to be summarized. It is irrespective of any domain.
Domain-specific: In this approach, the model acquires domain-specific points to create an accurate summary. An example is summarizing a research paper in a certain domain.
Query-based: It refers to a summary that addresses the query by extracting and presenting relevant information from the input documents saving the reader's time by presenting only the most pertinent information.

Steps involved in text summarization

The process of text summarization typically involves several steps, regardless of whether it is extractive or abstractive summarization. Here are the general steps involved in text summarization

While performing text summarization, the initial step is text cleaning or more generally called the text preprocessing step. In this cleaning step, we perform tasks like removing punctuation, converting to lowercase, and handling special characters. The next step is to split our text into sentences and then further into words. After that, we create a word-frequency count dictionary on whose basis the sentences are ranked. The most important sentences are identified, which are then included in the final summary.

State-of-the-art models and APIs

Now that we’ve overviewed text summarization for NLP, let's have a look at some of the best text summarization APIs and summarizers.

T5 (text-to-text transfer transformer)

It s a flexible language model that can be fine-tuned for various NLP tasks, including text summarization. It achieves strong performance in both extractive and abstractive summarization.

Hugging Face Transformer's API

Hugging Face provides a powerful Transformers library that includes a wide range of pre-trained models for text summarization.

Microsoft Azure text summarization

Azure's text summarising API, which is part of its text analytics package, provides attractive summarising for articles, papers, and documents. The API's pricing varies depending on usage and other requested features.

OpenAI GPT-3 API

OpenAI's GPT-3 (Generative Pre-trained Transformer 3) model can also be used for text summarization tasks. The GPT-3 API enables developers to utilize the model's capabilities for generating abstractive summaries.

Here's a table summarizing the extractive and abstractive summarization techniques and the approaches that are followed currently.

Summarization techniques

	*Technique*	Approach
Abstractive Summarization	Sentence scoring.	Assign scores to sentences based on specific criteria.
	Graph Algorithms.	Represent sentences as nodes in a graph and use centrality measures.
	Machine learning algorithms.	Train models to classify sentences as summary-worthy or non-summary-worthy.
Extractive Summarization	Natural language generation.	Generate summaries using linguistic rules and templates.
	Language models (e.g., GPT, BERT)	Pre-trained models that generate summaries based on learned patterns.
	Sequence-to-sequence models (e.g., encoder-decoder architectures)	Use encoder-decoder architectures to generate summaries.