Abstract
Information overload has been one of the challenges regarding information on the Internet. It is no longer a matter of information access, instead, the focus has shifted towards the quality of the retrieved data. Particularly in the news domain, multiple outlets report on the same news events but may differ in details. This work considers that different news outlets are more likely to differ in their writing styles and the choice of words, and proposes a method to extract sentences based on their key information by focusing on the shared synonyms in each sentence. Our method also attempts to reduce redundancy through hierarchical clustering and arrange selected sentences on the proposed orderBERT. The results show that the proposed unsupervised framework successfully improves the coverage and coherence, while also reducing the redundancy for a generated summary. Moreover, due to the process through which the dataset is obtained, a data refinement method is proposed to alleviate the problem of undesirable texts, which result from the process of automatic scraping.