Zenquiorra/large-scale-multimodal-multilingual-summarization-dataset

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The checks could not identify a recognised licence ID in the dataset's metadata.

Declared licence: Not declared in the tag or the card (see datasets with no declared licence)Evidence: README.md at 6c1eb75
Licence files: LICENSE, detected as mitEvidence: LICENSE at 6c1eb75
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Citation text from the card

@inproceedings{verma-etal-2023-large,
    title = "Large Scale Multi-Lingual Multi-Modal Summarization Dataset",
    author = "Verma, Yash  and
      Jangra, Anubhav  and
      Verma, Raghvendra  and
      Saha, Sriparna",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.eacl-main.263",
    pages = "3620--3632",
    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 Broadca

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dataset_licence({ "id": "Zenquiorra/large-scale-multimodal-multilingual-summarization-dataset" })
signal: "unclear"
stated_licence: null
sources: 0