Large Scale Multi-Lingual Multi-Modal Summarization Dataset

Yash Verma; Anubhav Jangra; Raghvendra Kumar; Sriparna Saha

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arXiv · 2302.06560

Large Scale Multi-Lingual Multi-Modal Summarization Dataset

Yash Verma·Anubhav Jangra·Raghvendra Kumar·Sriparna Saha

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Abstract

Significant developments in techniques such as encoder-decoder models have enabled us to represent information comprising multiple modalities. This information can further enhance many downstream tasks in the field of information retrieval and natural language processing; however, improvements in multi-modal techniques and their performance evaluation require large-scale multi-modal data which offers sufficient diversity. Multi-lingual modeling for a variety of tasks like multi-modal summarization, text generation, and translation leverages information derived from high-quality multi-lingual annotated data. In this work, we present the current largest multi-lingual multi-modal summarization dataset (M3LS), and it consists of over a million instances of document-image pairs along with a professionally annotated multi-modal summary for each pair. It is derived from news articles published by British Broadcasting Corporation(BBC) over a decade and spans 20 languages, targeting diversity across five language roots, it is also the largest summarization dataset for 13 languages and consists of cross-lingual summarization data for 2 languages. We formally define the multi-lingual multi-modal summarization task utilizing our dataset and report baseline scores from various state-of-the-art summarization techniques in a multi-lingual setting. We also compare it with many similar datasets to analyze the uniqueness and difficulty of M3LS.

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Yash Verma, Anubhav Jangra, Raghvendra Kumar, Sriparna Saha. 2023-02-13. Large Scale Multi-Lingual Multi-Modal Summarization Dataset. https://arxiv.org/abs/2302.06560

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