Text summarization is an NLP technique that extracts text from a large amount of data. It helps in creating a shorter version of the large text available.
It is important because :
Reduces reading time Helps in better research work Increases the amount of information that can fit in an area
Text Summarization steps
Obtain Data -> Text Preprocessing
-> Convert paragraphs to sentences
-> Tokenizing the sentences
-> Find weighted frequency of occurrence
-> Replace words by weighted frequency in sentences
-> Sort sentences in descending order of weights
-> Summarizing the Article
Why Summarize Texts
There are a number of valid reasons in favor of the automatic summarization of documents:
-> Summaries reduce reading time
-> When researching documents, summaries make the selection process easier.
-> Automatic summarization algorithms are less biased than human summarizers.
-> Personalized summaries are useful in question-answering systems as they provide personalized information.
-> Besides, a summary enables readers to identify the basic content of a document quickly and accurately, to determine its relevance to their interests, and thus to decide whether they need to read the document in its entirety.