Volume 9, Issue 3 (12-2023)
mmr 2023, 9(3): 111-135 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
rezghi M, mohammadian F, behzadi ebrahimi F. Automatic Text Summarization Based on The Power of Reconstructing Sentences from Each Other in a Sparse Reconstruction Framework. mmr 2023; 9 (3) :111-135
URL: http://mmr.khu.ac.ir/article-1-3116-en.html
URL: http://mmr.khu.ac.ir/article-1-3116-en.html
1- Tarbiat modares university , rezghi@modares.ac.ir
2- Tariat modares university
2- Tariat modares university
Abstract: (623 Views)
The rapid and continuous growth of the World Wide Web has made the process of extracting useful information with minimal volume of a large collection of documents a serious challenge these days. Summarizing documents are a very time-consuming and difficult task for humans, so it reveals the need for a powerful summarizing system to reduce the volume of texts and also to speed up access to useful information.
Recently, a summarization system based on the sparse representation approach has been presented, which tries to reconstruct each sentence in a sparse form by a linear combination of other sentences. In this approach, select a subset of sentences of the main text that contain important information about the text and send it to the output as a summary.
It is also necessary to select the least number of text sentences that have the maximum reconstruction of other text sentences, which achieves this goal by using the sparse representation approach.
This model consists of a penalty function based on the L2 norm to control sentence reconstruction and regularization.
The reconstruction function based on the L2 norm causes all the words to have an equal role in reconstructing sentences, which may cause outlier words to change the summarization result. Therefore, to improve the quality of the summary obtained in this article, we rewrite the penalty function with the L2 norm. This causes a different amount of error to be allocated for each of the words in sentence reconstruction, which causes the sensitivity of the method to be reduced to outlier words. The implementation results show that the proposed method provides a quick and high-quality summary based on the ROUGE and measure-F criteria compared to the previous methods.
Recently, a summarization system based on the sparse representation approach has been presented, which tries to reconstruct each sentence in a sparse form by a linear combination of other sentences. In this approach, select a subset of sentences of the main text that contain important information about the text and send it to the output as a summary.
It is also necessary to select the least number of text sentences that have the maximum reconstruction of other text sentences, which achieves this goal by using the sparse representation approach.
This model consists of a penalty function based on the L2 norm to control sentence reconstruction and regularization.
The reconstruction function based on the L2 norm causes all the words to have an equal role in reconstructing sentences, which may cause outlier words to change the summarization result. Therefore, to improve the quality of the summary obtained in this article, we rewrite the penalty function with the L2 norm. This causes a different amount of error to be allocated for each of the words in sentence reconstruction, which causes the sensitivity of the method to be reduced to outlier words. The implementation results show that the proposed method provides a quick and high-quality summary based on the ROUGE and measure-F criteria compared to the previous methods.
Type of Study: Research Paper |
Subject:
Mat
Received: 2020/07/21 | Revised: 2024/02/19 | Accepted: 2022/04/22 | Published: 2023/12/31 | ePublished: 2023/12/31
Received: 2020/07/21 | Revised: 2024/02/19 | Accepted: 2022/04/22 | Published: 2023/12/31 | ePublished: 2023/12/31
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
