Multi-Document News Web Page Summarization Using Content Extraction and Lexical Chain Based Key Phrase Extraction-Reference-Cited by-同舟云学术

Multi-Document News Web Page Summarization Using Content Extraction and Lexical Chain Based Key Phrase Extraction

Published:2023-04-07 Issue:8 Volume:11 Page:1762
ISSN:2227-7390
Container-title:Mathematics
language:en
Short-container-title:Mathematics

Author:

Arya Chandrakala¹,Diwakar Manoj²^ORCID,Singh Prabhishek³^ORCID,Singh Vijendra⁴,Kadry Seifedine⁵⁶⁷^ORCID,Kim Jungeun⁸

Affiliation:

1. School of Computing, Graphic Era Hill University, Dehradun 248002, India

2. CSE Department, Graphic Era Deemed to be University, Dehradun 248002, India

3. School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201009, India

4. School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India

5. Department of Applied Data Science, Noroff University College, 4608 Kristiansand, Norway

6. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates

7. Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon

8. Department of Software and CMPSI, Kongju National University, Cheonan 31080, Republic of Korea

Abstract

In the area of text summarization, there have been significant advances recently. In the meantime, the current trend in text summarization is focused more on news summarization. Therefore, developing a synthesis approach capable of extracting, comparing, and ranking sentences is vital to create a summary of various news articles in the context of erroneous online data. It is necessary, however, for the news summarization system to be able to deal with multi-document summaries due to content redundancy. This paper presents a method for summarizing multi-document news web pages based on similarity models and sentence ranking, where relevant sentences are extracted from the original article. English-language articles are collected from five news websites that cover the same topic and event. According to our experimental results, our approach provides better results than other recent methods for summarizing news.

Funder

Technology Development Program of MSS

the National Research Foundation of Korea (NRF) grant funded by the Korea government

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Link

https://www.mdpi.com/2227-7390/11/8/1762/pdf

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