Abstractive Summarization System

Abstract

The World Wide Web has evolved into one of the world's most extensive information and knowledge repositories. Despite their ease of access, the great majority of such individual publications are extremely difficult to analyse or evaluate. Text summaries assist users in achieving such information-seeking goals by providing rapid access to the highlights or important features of a document collection. Abstractive summarization attempts to reduce a given text to its core components based on the user's preference for brevity. To summarise, there are two approaches: extraction and abstraction. Statistical techniques are used for extracting most important sentences from a corpus. Abstraction entails reformulating material based on the type of summary. This approach makes use of more adaptive language processing technology. Despite the fact that abstraction yields better summaries, extraction remains the favoured strategy and is widely employed in research. A number of approaches, including cosine, can be used to calculate the measure of resemblance between articles. Sentences' statistical & linguistic features are utilised to determine their importance. An abstractive summary is used to absorb the fundamental concepts of a material and then summarise them into plain English.