Everything you always wanted to know about a dataset: Studies in data summarisation. Issue 135 (March 2020)

• Record Type: Journal Article
• Authors: Koesten, Laura; Simperl, Elena; Blount, Tom; Kacprzak, Emilia; Tennison, Jeni
• Is Part Of: International journal of human-computer studies. Issue 135(2020)
• Publication Date: 2020-03
• Subjects: Data summarisation -- Data search -- Data sensemaking -- Human data interaction; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2019.10.004
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: Text summaries of data are laid out according to common structures. Text summaries of data contain four main information types. Text summaries show a core set of dataset attributes. A number of summary features are not covered in general-purpose metadata schemas. We present a template for meaningful textual representations of data. Abstract: 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.