Everything you always wanted to know about a dataset: studies in data summarisation
This work addresses the problem of improving data search and comprehension for users like students and data professionals, but it is incremental as it builds on existing summarization concepts without introducing a new paradigm.
The paper tackled the problem of understanding what makes textual summaries of datasets meaningful for data discovery and sense-making, through two studies involving 69 students and 80 data-literate participants. The result was a template and guidelines for creating better summaries, based on qualitative analysis of information needs and dataset attributes.
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, which 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.