Review of automatic text summarization techniques & methods

It has been more than 50 years since the initial investigation on automatic text summarization was started. Various techniques have been successfully used to extract the important contents from text document to represent document summary. In this study, we review some of the studies that have been conducted in this still-developing research area. It covers the basics of text summarization, the types of summarization, the methods that have been used and some areas in which text summarization has been applied. Furthermore, this paper also reviews the significant efforts which have been put in studies concerning sentence extraction, domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it. In addition, the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study.

A Review on Automatic Text Summarization Approaches

Today there is a huge amount of information from a lot of various resources such as World Wide Web, news articles, e-books and emails. On the one hand, human beings face a shortage of time, and on the other hand, due to the social and occupational needs, they need to obtain the most important information from various resources. Automatic text summarization enables us to access the most important content in the shortest possible time. In this paper a query-oriented text summarization technique is proposed by extracting the most informative sentences. To this end, a number of features are extracted from the sentences, each of which evaluates the importance of the sentences from an aspect. In this paper 11 of the best features are extracted from each of the sentences. This paper has shown that use of more suitable features leads to improved summaries generated. In order to evaluate the automatic generated summaries, the ROUGE criterion has been used.

Query-oriented text summarization using sentence extraction technique

Text Summarization is the process of creating a condensed form of text document which maintains significant information and general meaning of source text. Automatic text summarization becomes an important way of finding relevant information precisely in large text in a short time with little efforts. Text summarization approaches are classified into two categories: extractive and abstractive. This paper presents the comprehensive survey of both the approaches in text summarization.

An overview of Text Summarization techniques

Text summarization is compress the source text into a diminished version conserving its information content and overall meaning. Because of the great amount of the information we are provided it and thanks to development of Internet Technologies, text summarization has become an important tool for interpreting text information. Text summarization methods can be classified into extractive and abstractive summarization. An extractive summarization method involves selecting sentences of high rank from the document based on word and sentence features and put them together to generate summary. The importance of the sentences is decided based on statistical and linguistic features of sentences. An abstractive summarization is used to understanding the main concepts in a given document and then expresses those concepts in clear natural language. In this paper, gives comparative study of various text summarization techniques.

/pdf/a-survey-automatic-text-summarization-184a0phyuk.pdf

A survey automatic text summarization

https://cyberleninka.org/article/n/1210912.pdf

S.A. Babar

Papers

Improving Performance of Text Summarization