PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India

Ashok Urlana, Pinzhen Chen, Zheng Zhao, Shay Cohen, Manish Shrivastava, Barry Haddow


Abstract
This paper introduces PMIndiaSum, a multilingual and massively parallel summarization corpus focused on languages in India. Our corpus provides a training and testing ground for four language families, 14 languages, and the largest to date with 196 language pairs. We detail our construction workflow including data acquisition, processing, and quality assurance. Furthermore, we publish benchmarks for monolingual, cross-lingual, and multilingual summarization by fine-tuning, prompting, as well as translate-and-summarize. Experimental results confirm the crucial role of our data in aiding summarization between Indian languages. Our dataset is publicly available and can be freely modified and re-distributed.
Anthology ID:
2023.findings-emnlp.777
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11606–11628
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.777
DOI:
10.18653/v1/2023.findings-emnlp.777
Bibkey:
Cite (ACL):
Ashok Urlana, Pinzhen Chen, Zheng Zhao, Shay Cohen, Manish Shrivastava, and Barry Haddow. 2023. PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11606–11628, Singapore. Association for Computational Linguistics.
Cite (Informal):
PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India (Urlana et al., Findings 2023)
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PDF:
https://aclanthology.org/2023.findings-emnlp.777.pdf
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 https://aclanthology.org/2023.findings-emnlp.777.mp4