Automatic Text Summarization: A State-of-the-Art Review

2020 ◽  
Author(s):  
Oleksandra Klymenko ◽  
Daniel Braun ◽  
Florian Matthes
Keyword(s):  
State Of The Art ◽  
Text Summarization ◽  
Automatic Text Summarization ◽  
Automatic Text

A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization

2021 ◽  
Vol 10 (2) ◽  
pp. 42-60
Author(s):  
Khadidja Chettah ◽  
Amer Draa
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Text Summarization ◽  
Automated System ◽  
Evaluation Metrics ◽  
Document Summarization ◽  
Automatic Text Summarization ◽  
Reference Methods ◽  
Textual Data ◽  
Automatic Text

Automatic text summarization has recently become a key instrument for reducing the huge quantity of textual data. In this paper, the authors propose a quantum-inspired genetic algorithm (QGA) for extractive single-document summarization. The QGA is used inside a totally automated system as an optimizer to search for the best combination of sentences to be put in the final summary. The presented approach is compared with 11 reference methods including supervised and unsupervised summarization techniques. They have evaluated the performances of the proposed approach on the DUC 2001 and DUC 2002 datasets using the ROUGE-1 and ROUGE-2 evaluation metrics. The obtained results show that the proposal can compete with other state-of-the-art methods. It is ranked first out of 12, outperforming all other algorithms.

scholarly journals Abstractive Summarization: A Survey of the State of the Art

2019 ◽  
Vol 33 ◽  
pp. 9815-9822 ◽  
Author(s):  
Hui Lin ◽  
Vincent Ng
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
The State ◽  
Text Summarization ◽  
Abstract Representation ◽  
Automatic Text Summarization ◽  
Input Text ◽  
Gradual Shift ◽  
Abstractive Summarization ◽  
Automatic Text

The focus of automatic text summarization research has exhibited a gradual shift from extractive methods to abstractive methods in recent years, owing in part to advances in neural methods. Originally developed for machine translation, neural methods provide a viable framework for obtaining an abstract representation of the meaning of an input text and generating informative, fluent, and human-like summaries. This paper surveys existing approaches to abstractive summarization, focusing on the recently developed neural approaches.

Multilingual Summarization Approaches

2014 ◽  
pp. 257-276
Author(s):  
Kamal Sarkar
Keyword(s):  
Information Overload ◽  
State Of The Art ◽  
Text Summarization ◽  
Automatic Text Summarization ◽  
On Line ◽  
Multilingual Text ◽  
Automatic Text ◽  
The Web

As the amount of on-line information in the languages other than English (such as Chinese, Japanese, German, French, Hindi, etc.) increases, systems that can automatically summarize multilingual documents are becoming increasingly desirable for managing information overload problem on the Web. This chapter presents an overview of automatic text summarization with special emphasis on multilingual text summarization. The various state-of-the-art multilingual summarization approaches have been grouped based on their characteristics and presented in this chapter.

scholarly journals Multilingual Summarization Approaches

2014 ◽  
pp. 158-177 ◽  
Author(s):  
Kamal Sarkar
Keyword(s):  
Information Overload ◽  
State Of The Art ◽  
Text Summarization ◽  
Automatic Text Summarization ◽  
On Line ◽  
Multilingual Text ◽  
Automatic Text ◽  
The Web

As the amount of on-line information in the languages other than English (such as Chinese, Japanese, German, French, Hindi, etc.) increases, systems that can automatically summarize multilingual documents are becoming increasingly desirable for managing information overload problem on the Web. This chapter presents an overview of automatic text summarization with special emphasis on multilingual text summarization. The various state-of-the-art multilingual summarization approaches have been grouped based on their characteristics and presented in this chapter.

Psychological Features for Automatic Text Summarization

2017 ◽  
Vol 25 (Suppl. 2) ◽  
pp. 129-149
Author(s):  
David E. Losada ◽  
Javier Parapar
Keyword(s):  
Empirical Study ◽  
Text Analysis ◽  
State Of The Art ◽  
Text Summarization ◽  
Learning Methods ◽  
Prediction Task ◽  
Automatic Text Summarization ◽  
Quantitative Text Analysis ◽  
Psychological Features ◽  
Automatic Text

Automatically summarizing a document requires conveying the important points of a large document in only a few sentences. Extractive strategies for summarization are based on selecting the most important sentences from the input document(s). We claim here that standard features for estimating sentence importance can be effectively combined with innovative features that encode psychological aspects of communication. We employ Quantitative Text analysis tools for estimating psychological features and we inject them into state-of-the-art extractive summarizers. Our experiments demonstrate that this novel set of features is a good guidance for selecting salient sentences. Our empirical study concludes that psychological features are best suited for hard summarization cases. This motivated us to formally define and study the problem of predicting the difficulty of summarization. We propose a number of predictors to model the difficulty of every summarization problem and we evaluate several learning methods to perform this prediction task.

Sign in / Sign up

Export Citation Format

Share Document