A Review of State-Of-The-Art Automatic Text Summarisation

Abstract

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. The first method chooses the relevant statements out of the given input text or document & convolves those statements to create the final output as summary. The second method converts the input document into an intermedial representation before generating a summary containing phrases that differ from the originals. Both the extractive and abstractive processes are used in the hybrid method. Despite all of the methodologies presented, the produced summaries still lag behind human-authored summaries. By addressing the various components of ATS approaches, methodologies, techniques, datasets, assessment methods and future research goals, this study provides a thorough review for researchers and novices in the field of NLP.

Keywords: Text Summarisation, Abstractive, Extractive, Hybrid, Dataset