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  license: cc-by-sa-4.0
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  license: cc-by-sa-4.0
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+
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+ # Dataset
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+ ## Topic-exclusive split
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+ The training dataset contains popular topics (instances), while other topics are in the test and validation datasets.
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+ * test_diff.json
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+ * training_diff.json
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+ * validation_diff.json
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+
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+ ## Topic-independent split
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+ Topics are randomly selected in datasets. For a common purpose, we suggest THESE DATASETS.
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+ * test_random.json
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+ * training_random.json
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+ * validation_random.json
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+
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+ # GitHub
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+ * https://github.com/declare-lab/WikiDes/
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+
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+
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+ # Citation
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+
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+ ## APA
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+ Ta, H. T., Rahman, A. B. S., Majumder, N., Hussain, A., Najjar, L., Howard, N., ... & Gelbukh, A. (2022). WikiDes: A Wikipedia-based dataset for generating short descriptions from paragraphs. *Information Fusion*.
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+
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+ ## BibTeX
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+ ```
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+ @article{Ta_2022,
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+ doi = {10.1016/j.inffus.2022.09.022},
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+ url = {https://doi.org/10.1016%2Fj.inffus.2022.09.022},
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+ year = 2022,
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+ month = {sep},
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+ publisher = {Elsevier {BV}},
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+ author = {Hoang Thang Ta and Abu Bakar Siddiqur Rahman and Navonil Majumder and Amir Hussain and Lotfollah Najjar and Newton Howard and Soujanya Poria and Alexander Gelbukh},
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+ title = {{WikiDes}: A Wikipedia-based dataset for generating short descriptions from paragraphs},
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+ journal = {Information Fusion}}
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+ ```
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+
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+ # Paper links
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+ * https://doi.org/10.1016%2Fj.inffus.2022.09.022
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+ * https://arxiv.org/abs/2209.13101
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+
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+ # Contact
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+ Hoang Thang Ta, tahoangthang@gmail.com