Summary of L3cube-mahasum: a Comprehensive Dataset and Bart Models For Abstractive Text Summarization in Marathi, by Pranita Deshmukh et al.
L3Cube-MahaSum: A Comprehensive Dataset and BART Models for Abstractive Text Summarization in Marathi
by Pranita Deshmukh, Nikita Kulkarni, Sanhita Kulkarni, Kareena Manghani, Raviraj Joshi
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents the MahaSUM dataset, a large-scale collection of news articles in Marathi for training and evaluating abstractive summarization models. The dataset contains 25k samples created by scraping online news sources and manually verifying abstract summaries. Additionally, the authors train an IndicBART model using this dataset, demonstrating its effectiveness in producing high-quality summaries in Marathi. This work contributes to NLP research in Indic languages, providing a valuable resource for future studies using state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a big collection of news articles in the Marathi language. They want to help machines summarize news stories better. To do this, they collected 25,000 news articles and checked each one to make sure it was accurate. Then, they used these articles to train a special kind of computer model that can summarize news stories. This model is good at making summaries in Marathi, which is important for people who want to understand news from India. |
Keywords
» Artificial intelligence » Nlp » Summarization
