Automatic Text Summarization using Kernel Ridge Regression

• Published in: Proceedings (International Symposium on Symbolic and Numeric Algorithms for Scientific Computing) pp. 202 - 209
• Main Authors: Onita, Daniela, Cucu, Ciprian
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.09.2023
• Subjects: kernel ridge regression; text summarization
• ISSN: 2470-881X
• DOI: 10.1109/SYNASC61333.2023.00034

Social networks are full of news, opinions, or research studies, however, some of the information provided might not be completely true. Furthermore, some people can be easily influenced to take over other people's beliefs without researching the veracity of the idea. This leads to the existence of various conspiracy theories. In this era of abundant information existent about any subject, it is very important to be correctly informed. In this work, we investigate several Facebook posts which belong to various conspiracy theories. Our goal is to develop a system that can automatically summarize the information from these posts more objectively. For this, we use a Kernel Ridge Regression(KRR) model which transforms a subjective post into another text description that is more objective. We collected a data corpus from Facebook which contains written posts to misinform the population about various conspiracy theories, such as population control through chips in vaccines, controlling the virus through 5G, the virus being created in a lab by Americans / Chinese, the face masks are dangerous, the government wants to impose a dictatorship, the reporting of cases is wrong, the COVID-19 pandemic, and so on. The posts were generally collected from the same Facebook accounts, which belong to some influencers and public figures. We compared our proposed method used to generate a summary with a deep learning approach. The experimental results show that our proposed method for generating text summaries performs better than applied deep-learning approaches. Furthermore, the proposed model generated similar words to the original posts.