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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics: EMNLP 2023
EditorsHouda Bouamor, Juan Pino, Kalika Bali
Place of PublicationSingapore
PublisherAssociation for Computational Linguistics
Pages11606-11628
Number of pages23
ISBN (Print)979-8-89176-061-5
DOIs
Publication statusPublished - 1 Dec 2023
EventThe 2023 Conference on Empirical Methods in Natural Language Processing - , Singapore
Duration: 6 Dec 202310 Dec 2023
https://2023.emnlp.org/

Conference

ConferenceThe 2023 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2023
Country/TerritorySingapore
Period6/12/2310/12/23
Internet address

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