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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44245

Optimization-Based Techniques for Sentiment-Oriented Text Summarization: A Concise Review

Abeer Raad, Rafid Sagban

Abstract

In natural language processing (NLP), text summarization is a process of converting a big textual information from single or multi documents into a concise text without change its semantics. The variety of summarization procedures in literature leads to different processes have their own pros and cons. Text summarization expressed as an NPcomplete problem. Thus, optimization-based summarization methodologies is the only available framework for solving such kind of mathematical problems in A.I. This paper reviews algorithms that express the problem in such a way that optimize text summarization to get high accuracy.

Keywords

document summarization, optimization-based, Genetic Algorithm, Particle Swarm Optimization, Ant colony optimization, Artificial Bee colony

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