Extraction and attribution of public figures statements for journalism in Indonesia using deep learning

• Published in: Knowledge-based systems 2024-04, Vol.289, p.111558, Article 111558
• Main Authors: Purnomo W.P., Yohanes Sigit, Kumar, Yogan Jaya, Zulkarnain, Nur Zareen, Raza, Basit
• Format: Article
• Language: English
• Subjects: Deep learning; Indonesian language; Knowledge-based; Named-entity recognition; Statement extraction and attribution
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2024.111558

•Public figures' statements in news articles are valuable.•Named entity recognition could be used to extract and attribute the public figure's statements.•RNN and transformer-based extraction models performed well with the highest F1 score 81.34 % and 81.01 %.•The size of the corpus affects the performance of the model. News articles are usually written by journalists based on statements taken from interviews with public figures. Attribution from such statements provides important information and it can be extracted from news articles to build a knowledge base by developing a sequential tagging scheme such as entity recognition. This research applies two deep learning architectures: recurrent neural networks-based and transformer-based, to establish public figures statement attribution and extraction models in the Indonesian Language. The experiments are conducted using five deep-learning model architectures with two different corpus sizes to investigate the impact of corpus size on each model's performance. The experiments show that the best model for the RNN-based architecture is PFSA-ID-BLWCA which achieves 81.34 % F1 score, and the best model for the transformer-based is PFSA-ID-TWCA which obtains 81.01 % F1 score. This research also discovers that the size of the corpus influences the model performances. Furthermore, the study lays a foundation to overcome the attribution extraction in another language, especially low-resource languages, with some necessary adjustments.