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Everything you always wanted to know about a dataset: studies in data summarisation

Research output: Contribution to journalArticlepeer-review

Laura Koesten, Elena Simperl, Magdalena Kacprzak Emilia, Thomas Blount, Jeni Tennison

Original languageEnglish
Article number102367
Pages (from-to)1-21
Early online date14 Oct 2019
Accepted/In press14 Oct 2019
E-pub ahead of print14 Oct 2019
PublishedMar 2020


King's Authors


Summarising data as text helps people make sense of it. It also improves data discovery, as search algorithms can match this text against keyword queries. In this paper, we explore the characteristics of text summaries of data in order to understand how meaningful summaries look like. We present two complementary studies: a data-search diary study with 69 students, which offers insight into the information needs of people searching for data; and a summarisation study, with a lab and a crowdsourcing component with overall 80 data-literate participants, who produced summaries for 25 datasets. In each study we carried out a qualitative analysis to identify key themes and commonly mentioned dataset attributes, which people consider when searching and making sense of data. The results helped us design a template to create more meaningful textual representations of data, alongside guidelines for improving data-search experience overall.

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