## 4 News Extraction Results
Collaborative filtering techniques encounter several challenges, as they heavily rely on user opinions, demographics, and feedback to establish similarity [1, 34]. The dynamic nature of both users and news articles complicates the accurate modeling of user preferences based on their past readings [35, 36, 37]. User preferences and interests can change over time due to current events and the popularity of news articles [38]. Furthermore, users tend to be hesitant in actively clicking or recommending news articles while browsing specific topics [39]. On the other hand, content-based approaches recommend new items to users by calculating the similarity between the features of previously selected items. However, content-based approaches encounter difficulties in determining the similarity between news articles that cover diverse topics and in accounting for potentially hidden factors that influence user choices [40]. While most studies have focused on runtime similarity between recent articles, the approach adopted by the DNSA involves linking stories during preservation to ensure long-term accessibility and to benefit future generations. The common ratio measure for stories (CRMS) is a content-based similarity measure that has proven to be effective in linking digital news articles during preservation. The Digital News Story Archive (DNSA), ensures the future accessibility of related news articles by preserving and formatting linked news articles sourced from a vast corpus of news articles extracted from multiple sources. To determine the similarity among related news articles in the archive, a threshold value for CT/TT (a measure defined in the study) was established, representing the best indicator of similarity [27]. The CRMS is evaluated extensively in comparison to the baseline similarity measure, namely the cosine similarity measure (CSM). Additionally, the CRMS is subjected to analysis based on human judgment through user-based evaluation, providing empirical insights. The CRMS emerges as the most suitable measure for linking news articles, particularly facilitating the linkage of dual-language news articles gathered from diverse sources during preservation and the creation of archives. The results of this evaluation and analysis are derived from a dataset of 5.3K news articles extracted from ten different news sources over ten days. Similarly, another content-based similarity measure is introduced based on the use of transliteration words known as the similarity measure based on transliteration words (SMTW). It is observed that the use of English transliteration words is common in Urdu manuscripts. This practice is anticipated to play a vital role in linking Urdu news articles with English news articles. These linking mechanisms link formatted news articles to ensure the future accessibility of related news articles from an enormous corpus of news articles extracted from multiple sources in the DNSA [41]. A comprehensive and empirical evaluation was conducted to assess the effectiveness of the SMTW. The results demonstrate that the SMTW is the most viable metric for linking news articles. It strongly promotes the linking of dual-language news articles obtained from various sources during the process of preservation and archive creation. \"Transliteration is a process of using the text of one script in another script or the process of converting text from one language to another\". In the field of linguistics, the act of incorporating a word or a group of words from one language into another language's writing scripts is known as borrowing. These borrowed words are commonly referred to as loan words [42]. When it comes to transliteration, the process of representing a phrase or word from one language to another that uses a different writing system, challenges may arise, especially if the languages in question have distinct sounds and writing scripts [43, 44].