A Review of State-of-the-art Automatic Text Summarisation
$ 38.5
Description
Text summarisation comes under the domain of Natural Language Processing (NLP), which entails replacing a long, precise and concise text with a shorter, precise and concise one. Manual text summarising takes a lot of time, effort and money and it's even unfeasible when there's a lot of text. Much research has been conducted since the 1950s and researchers are still developing Automatic Text Summarisation (ATS) systems. In the past few years, lots of text-summarisation algorithms and approaches have been created. In most cases, summarisation algorithms simply turn the input text into a collection of vectors or tokens. The basic objective of this research is to review the different strategies used for text summarising. There are three types of ATS approaches, namely: Extractive text summarisation approach, Abstractive text summarisation approach and Hybrid text summarisation approach.