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Exploring News Summarization and Enrichment in a Highly Resource-Scarce Indian Language: A Case Study of Mizo

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arxiv 2405.00717 v1 pith:Q65SMNXB submitted 2024-04-25 cs.CL cs.AI

Exploring News Summarization and Enrichment in a Highly Resource-Scarce Indian Language: A Case Study of Mizo

classification cs.CL cs.AI
keywords mizonewsinformationarticleslanguagescorrespondingenrichmentevents
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Obtaining sufficient information in one's mother tongue is crucial for satisfying the information needs of the users. While high-resource languages have abundant online resources, the situation is less than ideal for very low-resource languages. Moreover, the insufficient reporting of vital national and international events continues to be a worry, especially in languages with scarce resources, like \textbf{Mizo}. In this paper, we conduct a study to investigate the effectiveness of a simple methodology designed to generate a holistic summary for Mizo news articles, which leverages English-language news to supplement and enhance the information related to the corresponding news events. Furthermore, we make available 500 Mizo news articles and corresponding enriched holistic summaries. Human evaluation confirms that our approach significantly enhances the information coverage of Mizo news articles. The mizo dataset and code can be accessed at \url{https://github.com/barvin04/mizo_enrichment

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