Automatic Text Summarization Based on Lexical Chains and Structural Features

2007 ◽  
Author(s):  
Lei Yu ◽  
Jia Ma ◽  
Fuji Ren ◽  
Shingo Kuroiwa
Keyword(s):  
Structural Features ◽  
Text Summarization ◽  
Automatic Text Summarization ◽  
Lexical Chains ◽  
Automatic Text

scholarly journals Efficiently Computed Lexical Chains as an Intermediate Representation for Automatic Text Summarization

2002 ◽  
Vol 28 (4) ◽  
pp. 487-496 ◽  
Author(s):  
H. Gregory Silber ◽  
Kathleen F. McCoy
Keyword(s):  
Linear Time ◽  
Time Algorithm ◽  
Text Summarization ◽  
Intermediate Step ◽  
Intermediate Representation ◽  
Efficient Tool ◽  
Automatic Text Summarization ◽  
Lexical Chains ◽  
Lexical Chain ◽  
Automatic Text

While automatic text summarization is an area that has received a great deal of attention in recent research, the problem of efficiency in this task has not been frequently addressed. When the size and quantity of documents available on the Internet and from other sources are considered, the need for a highly efficient tool that produces usable summaries is clear. We present a linear-time algorithm for lexical chain computation. The algorithm makes lexical chains a computationally feasible candidate as an intermediate representation for automatic text summarization. A method for evaluating lexical chains as an intermediate step in summarization is also presented and carried out. Such an evaluation was heretofore not possible because of the computational complexity of previous lexical chains algorithms.

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.

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