TY - GEN

T1 - Summarization Algorithms for News

T2 - 10th International Conference on Computational Data and Social Networks, CSoNet 2021

AU - Gadasina, Lyudmila

AU - Veklenko, Vladislav

AU - Luukka, Pasi

N1 - Publisher Copyright: © 2021, Springer Nature Switzerland AG.

PY - 2021

Y1 - 2021

N2 - Extract summarization algorithms help identify significant information from the news by extracting meaningful sentences from the original text. The information background existing at the time of the news release often significantly affects its content. Such background can distort the text summarization algorithm working results. The study was conducted with the example of the theme “coronavirus” (COVID-19), which at the time of the study was one of the main topics in news feeds. Experiments were carried out on sports news articles, concerned football. This news area was selected because it is not related to medical topics. The TextRank algorithm for sport news extraction was applied in two ways. First, the key information from the source text of news was extracted. Then, a list of the COVID related words was created and the key information from news without considering words from this list was extracted. Our approach showed that mentioning a popular theme such as COVID that is not related to sports can have a negative impact on the text summarization algorithm. We suggest that to obtain accurate results of the algorithm operation, it is necessary to first compile a dictionary of terms related to the coronavirus theme and then exclude them when identifying the main content of news texts.

AB - Extract summarization algorithms help identify significant information from the news by extracting meaningful sentences from the original text. The information background existing at the time of the news release often significantly affects its content. Such background can distort the text summarization algorithm working results. The study was conducted with the example of the theme “coronavirus” (COVID-19), which at the time of the study was one of the main topics in news feeds. Experiments were carried out on sports news articles, concerned football. This news area was selected because it is not related to medical topics. The TextRank algorithm for sport news extraction was applied in two ways. First, the key information from the source text of news was extracted. Then, a list of the COVID related words was created and the key information from news without considering words from this list was extracted. Our approach showed that mentioning a popular theme such as COVID that is not related to sports can have a negative impact on the text summarization algorithm. We suggest that to obtain accurate results of the algorithm operation, it is necessary to first compile a dictionary of terms related to the coronavirus theme and then exclude them when identifying the main content of news texts.

KW - Coronavirus

KW - Extracting

KW - News

KW - Summarization algorithm

KW - Text

UR - http://www.scopus.com/inward/record.url?scp=85121872878&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-91434-9_30

DO - 10.1007/978-3-030-91434-9_30

M3 - Conference contribution

AN - SCOPUS:85121872878

SN - 9783030914332

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 351

EP - 360

BT - Computational Data and Social Networks - 10th International Conference, CSoNet 2021, Proceedings

A2 - Mohaisen, David

A2 - Jin, Ruoming

PB - Springer Nature

Y2 - 15 November 2021 through 17 November 2021

ER -