L3Cube-MahaSum: A Comprehensive Dataset and BART Models for Abstractive Text Summarization in Marathi
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Abstract (arXiv)
We present the MahaSUM dataset, a large-scale collection of diverse news articles in Marathi, designed to facilitate the training and evaluation of models for abstractive summarization tasks in Indic languages. The dataset, containing 25k samples, was created by scraping articles from a wide range of online news sources and manually verifying the abstract summaries. Additionally, we train an IndicBART model, a variant of the BART model tailored for Indic languages, using the MahaSUM dataset. We evaluate the performance of our trained models on the task of abstractive summarization and demonstrate their effectiveness in producing high-quality summaries in Marathi. Our work contributes to the advancement of natural language processing research in Indic languages and provides a valuable resource for future research in this area using state-of-the-art models. The dataset and models are shared publicly at https://github.com/l3cube-pune/MarathiNLP
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 11 Oct 2024.
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5 of 13 references.
References · 5
- 2021XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languagescited by 347
- 2022L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resourcescited by 51
- 2021IndicBART: A Pre-trained Model for Indic Natural Language Generationcited by 39
- 2022L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Librarycited by 31
- 2023Question answering using deep learning in low resource Indian language Marathicited by 4
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