An Overview of Semantic Based Document Summarization in Different Languages

Abstract

The evolution of Artificial Intelligence (AI) gave insight into how humans should interact with machines. Natural Language Processing (NLP) is an area under AI, which aims to minimize the communication gap between human and machine. Text summarization is a sub-domain of NLP, which primarily focuses on summarizing text documents into a meaningful summary. Text summarization is useful in the areas, such as media monitoring, search engine optimization (SEO), question answering systems, etc. Lots of research works are carried out in the area of text summarization. A majority of the research works are adopted syntactic approaches. However, the semantic based approaches provide more meaningful summary compared to syntactic approaches. Due to the complexity of the semantic based methods, only few works are reported especially in Indian languages. In this paper, an overview of various semantic based text summarization methods are explored. An extensive review is done in languages, such as English, Arabic, Chinese, Hindi, Kannada, Tamil, and Malayalam. At the end of this survey, a critical analysis was done on various semantic based text summarization techniques. This analysis will not only give insights to potential prospective researchers, but also come up with an improved framework or method for semantic based abstractive text summarization.