A System for Assisting Users in Automatically Obtaining Comprehensive and Condensed Information About an Event from Various Sources

In today’s world, the majority of newspapers utilize social media platforms to distribute their published information. Thus, users have available a huge amount of news on the same topic. Nevertheless, they prefer to avoid the arduous task of reading every single news article for forming their independent own opinion. To address this challenge, this paper proposes a system that exploits transformer neural models to discern the differences in content between an article read by a user and a collection of related articles about the same topic, and text summarization models to summarize these differences. Thus, the user can read only the summary rather than all the articles still acquiring the different opinions on the topic from the different sources without the burden of reading all the related articles. The system has been tested against human opinion collected through crowdsourcing, obtaining an average F1-Score of 0.753, and compared with a BERT classifier which achieved similar accuracy with an average F1-score of 0.754. In our discussion, we delve into the advantages and critical issues that emerged during the evaluation, along with potential future directions.