  As free online encyclopedias with massive volumes of content, Wikipedia and
Wikidata are key to many Natural Language Processing (NLP) tasks, such as
information retrieval, knowledge base building, machine translation, text
classification, and text summarization. In this paper, we introduce WikiDes, a
novel dataset to generate short descriptions of Wikipedia articles for the
problem of text summarization. The dataset consists of over 80k English samples
on 6987 topics. We set up a two-phase summarization method - description
generation (Phase I) and candidate ranking (Phase II) - as a strong approach
that relies on transfer and contrastive learning. For description generation,
T5 and BART show their superiority compared to other small-scale pre-trained
models. By applying contrastive learning with the diverse input from beam
search, the metric fusion-based ranking models outperform the direct
description generation models significantly up to 22 ROUGE in topic-exclusive
split and topic-independent split. Furthermore, the outcome descriptions in
Phase II are supported by human evaluation in over 45.33% chosen compared to
23.66% in Phase I against the gold descriptions. In the aspect of sentiment
analysis, the generated descriptions cannot effectively capture all sentiment
polarities from paragraphs while doing this task better from the gold
descriptions. The automatic generation of new descriptions reduces the human
efforts in creating them and enriches Wikidata-based knowledge graphs. Our
paper shows a practical impact on Wikipedia and Wikidata since there are
thousands of missing descriptions. Finally, we expect WikiDes to be a useful
dataset for related works in capturing salient information from short
paragraphs. The curated dataset is publicly available at:
https://github.com/declare-lab/WikiDes.
