Metadata for Efficient Management of Digital News Articles in Multilingual News Archives

Abstract

The digital news preservation and management of low-resource languages are challenging tasks, especially in vast collections. Unique identification of individual digital objects is possible with well-defined attributes to assure efficient management, such as access, retrieval, preservation, usability, and transformability. The metadata element set is required to maximize the available attributes related to the digital objects. To create a comprehensive metadata set that contains all the necessary attributes and data about the digital news objects. It is more challenging and complicated when the archive contains articles from low-resourced and morphologically complex languages like Urdu and Arabic, which is difficult for machines to understand. The study presents challenges in low-resource languages (LRL) and research challenges. This metadata will help to link news articles based on similarity with other news articles stored in the digital news stories archive (DNSA) and ensures accessibility. In this study, we introduced 38 metadata elements set for the digital news stories preservation (DNSP) framework, of which 16 are explicit and 12 are implicit metadata elements. The paper presents how the digital news stories archive (DNSA) is enhanced to a multilingual archive and discusses the digital news stories extractor, which addresses major issues in implementing low-resource languages and facilitates normalized format migration. The extraction results are presented in detail for high-resource languages, that is, English, and low-resource languages (HRL), that is, Urdu and Arabic. The LRL encountered a high error rate during preservation compared to HRL, 10%, and 03%, respectively. The metadata extraction results show that HRL sources support all metadata elements as compared to LRL. The LRL has good support for explicit meta elements and many implicit meta elements with low extraction percentages. The LRL needs a more detailed study for accurate news content extraction and archiving for future access.