Text summarization refers to the technique of distilling lengthy text into a succinct and coherent summary, emphasizing the principal points delineated in the document. This is a prevalent issue in machine learning and natural language processing (NLP), with the goal being to abbreviate extensive texts effectively. There are two primary strategies for automatic summarization:
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Extraction-based Summarization: This method entails extracting crucial phrases from the original document and amalgamating them to form a summary without modifying the text. The focus here is on drawing out significant sections word-for-word.
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Abstraction-based Summarization: This approach involves paraphrasing and condensing parts of the original document to create a summary. While abstraction can rectify grammar inconsistencies, it poses more developmental challenges compared to extraction.
Algorithms for text summarization typically function as supervised machine learning problems, where models are educated to comprehend documents and extract valuable information for summarization purposes. The procedure includes identifying key phrases, educating a machine learning classifier, and subsequently generating summaries based on these identified key phrases.
