Summarizing Indian Languages using Multilingual Transformers based Models
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Abstract (arXiv)
With the advent of multilingual models like mBART, mT5, IndicBART etc., summarization in low resource Indian languages is getting a lot of attention now a days. But still the number of datasets is low in number. In this work, we (Team HakunaMatata) study how these multilingual models perform on the datasets which have Indian languages as source and target text while performing summarization. We experimented with IndicBART and mT5 models to perform the experiments and report the ROUGE-1, ROUGE-2, ROUGE-3 and ROUGE-4 scores as a performance metric.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 17 references that are in the map. First posted on arXiv 29 Mar 2023.
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13 of 17 references.
References · 13
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- 2020Longformer: The Long-Document Transformercited by 3,362
- 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
- 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
- 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,028
- 2019Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelcited by 431
- 2021XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languagescited by 346
- 2021LongT5: Efficient Text-To-Text Transformer for Long Sequencescited by 232
- 2020WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarizationcited by 154
- 2019On the Use of ArXiv as a Datasetcited by 101
- 2020A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portalcited by 72
- 2021PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarizationcited by 62
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.