Text summarization is the technique for generating a concise and precise summary of voluminous texts while focusing on the sections that convey useful information, and without losing the overall meaning.
Automatic text summarization aims to transform lengthy documents into shortened versions, something which could be difficult and costly to undertake if done manually.
Machine learning algorithms can be trained to comprehend documents and identify the sections that convey important facts and information before producing the required summarized texts.
The main types of text summarization
Broadly, there are two approaches to summarizing texts in NLP: extraction and abstraction.
Extraction-based summarization
In extraction-based summarization, a subset of words that represent the most important points is pulled from a piece of text and combined to make a summary. Think of it as a highlighter—which selects the main information from a source text.
In machine learning, extractive summarization usually involves weighing the essential sections of sentences and using the results to generate summaries.
Different types of algorithms and methods can be used to gauge the weights of the sentences and then rank them according to their relevance and similarity with one another—and further joining them to generate a summary.
Abstraction-based summarization
In abstraction-based summarization, advanced deep learning techniques are applied to paraphrase and shorten the original document, just like humans do. Since abstractive machine learning algorithms can generate new phrases and sentences that represent the most important information from the source text, they can assist in overcoming the grammatical inaccuracies of the extraction techniques.
Although abstraction performs better at text summarization, developing its algorithms requires complicated deep learning techniques and sophisticated language modeling.
Abstraction-based summarization approaches must address a wide variety of NLP problems, such as natural language generation, semantic representation, and inference permutation.
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