Benchmarking NAS for Article Separation in Historical Newspapers

Abstract

The digitization of historical newspapers is a crucial task for preserving cultural heritage and making it accessible for various natural language processing and information retrieval tasks. One of the key challenges in digitizing old newspapers is article separation, which consists of identifying and extracting individual articles from scanned newspaper images and retrieving the semantic structure. It is a critical step in making historical newspapers machine-readable and searchable, enabling tasks such as information extraction, document summarization, and text mining. In this work, we assess NewsEye Article Separation (NAS), a multilingual dataset for article separation in historical newspapers. It consists of scanned newspaper pages from the $19^{th}$ and $20^{th}$ centuries and annotation files in German, Finnish, and French. Moreover, the dataset is challenging due to the varying layouts and font styles, which makes it difficult for models to generalize to unseen data. Also, we introduce new metrics of article error rate, article coverage score, proper predicted article, and segmentation to evaluate the performance of the models trained on the NAS to highlight the relevance and challenges of this dataset. We believe that NAS, which is publicly available, will be a valuable resource for researchers working on historical newspaper digitization.