TITLE:
LbrBart: A Text Summarizer for Liberia News Outlets
AUTHORS:
Mark T. Mangou, Cong Wu, Ousmane Sangary
KEYWORDS:
LbrBart, Pseudo-Summarization, Text Summarization, Low-Resource, Abstractive Summarization
JOURNAL NAME:
Open Access Library Journal,
Vol.12 No.3,
March
26,
2025
ABSTRACT: The deluge of digital information in Liberia necessitates efficient content consumption. LbrBart, a proposed text summarization tool, is specifically designed to cater to the needs of Liberian news outlets, including the Liberian Observer, FrontPage Africa, AllAfrica, The Inquirer and Liberia News Agency (LINA). Employing advanced techniques like pseudo-summarization, centrality-based sentence recovery, and low-resource abstractive summarization using the BART model, LbrBart aims to significantly enhance the readability and accessibility of Liberian news content. This study explores the adaptation of these methodologies to the unique linguistic and cultural nuances of Liberian writing. We utilize established evaluation metrics to assess LbrBart’s performance against state-of-the-art summarization benchmarks, ensuring the preservation of core article meanings while significantly reducing reading time. To evaluate LbrBart’s capabilities, we have compared it with state-of-the-art models and the results show better performance over baselines. Addressing information overload and improving information dissemination in Liberia, LbrBart not only benefits individual readers but also contributes to enhanced public engagement and informed citizenship. This research marks a significant step forward in harnessing technology to meet the specific needs of local news consumers, setting a precedent for future advancements in natural language processing tailored to emerging markets. Experiments on baseline datasets demonstrate competitive performance compared to previous studies.