Review of ambiguity problem in text summarization using hybrid ACA and SLR

Abstract

Text summarization is the process of creating a text summary that contains important information from a text document. In recent years, significant progress has been made in the field of text summarization research, along with the challenges that drive research progress in the field at large. The development of textual data has sparked great interest in text summarization research, which is thoroughly reviewed in this survey study. Text summarization research improvements continue to be made to date with various approaches, such as abstractive and extractive. The abstractive approach uses an intermediate representation of the input document to produce a summary that may differ from the original text. The extractive approach means that key sentences are extracted from the source document and combined to form a summary. Despite the various methodologies and approaches recommended, the summaries produced still contain ambiguities that can be interpreted with different meanings, resulting in errors in defining ambiguities, uncertainty in measuring the quality of summaries, difficulty in modeling linguistic context, difficulty in representing semantic meanings, and difficulty in specifying types of ambiguities. This research survey offers a comprehensive exploration of text summarization research, covering challenges, classifications, approaches, preprocessing methods, features, techniques, and evaluation methods, meeting future research needs. The results provide an overview of the state of the art of recent research developments in the topic of ambiguity resolution in text summarization, such as trends in research topics and approaches or techniques used in addressing ambiguity problems in text summarization.