Exploring News Summarization and Enrichment in a Highly Resource-Scarce Indian Language: A Case Study of Mizo
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Abstract (arXiv)
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 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 urlhttps://github.com/barvin04/mizoenrichment
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 6 of its 14 references that are in the map. First posted on arXiv 25 Apr 2024.
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References · 6
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,884
- 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
- 2019Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelcited by 429
- 2019Hierarchical Transformers for Multi-Document Summarizationcited by 132
- 2018Ranking Sentences for Extractive Summarization with Reinforcement Learningcited by 128
- 2018Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervisedcited by 75
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