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As free online encyclopedias with massive volumes of content, Wikipedia andWikidata are key to many Natural Language Processing (NLP) tasks, such asinformation retrieval, knowledge base building, machine translation, textclassification, and text summarization. In this paper, we introduce WikiDes, anovel dataset to generate short descriptions of Wikipedia articles for theproblem of text summarization. The dataset consists of over 80k English sampleson 6987 topics. We set up a two-phase summarization method - descriptiongeneration (Phase I) and candidate ranking (Phase II) - as a strong approachthat relies on transfer and contrastive learning. For description generation,T5 and BART show their superiority compared to other small-scale pre-trainedmodels. By applying contrastive learning with the diverse input from beamsearch, the metric fusion-based ranking models outperform the directdescription generation models significantly up to 22 ROUGE in topic-exclusivesplit and topic-independent split. Furthermore, the outcome descriptions inPhase II are supported by human evaluation in over 45.33% chosen compared to23.66% in Phase I against the gold descriptions. In the aspect of sentimentanalysis, the generated descriptions cannot effectively capture all sentimentpolarities from paragraphs while doing this task better from the golddescriptions. The automatic generation of new descriptions reduces the humanefforts in creating them and enriches Wikidata-based knowledge graphs. Ourpaper shows a practical impact on Wikipedia and Wikidata since there arethousands of missing descriptions. Finally, we expect WikiDes to be a usefuldataset for related works in capturing salient information from shortparagraphs. The curated dataset is publicly available at:https://github.com/declare-lab/WikiDes.

WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs