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

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, 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.

Dataset search: a survey

“…Choosing a dataset greatly depends on the information provided alongside it. A number of studies indicate that standard metadata does not provide sufficient information for dataset reuse [81,106]. Recent studies have discussed textual ( [81,129]) or visual [138] surrogates of datasets that aim to help people identify relevant documents and increase accuracy and/or satisfaction with their relevance judgments.…”

“…Users judge the relevance of datasets for a specific task based on the dataset's scope (e.g. geographical and temporal scope) [104,75], basic statistics about the dataset such as counts and value ranges, and information about granularity of information in the data [81]. The documentation of variables and the context from which the dataset comes from also play a key role.…”

“…Summarization and Annotations. To help both search and user understanding, summarizations and annotations are additional metadata that can be generated about the underlying dataset [81]. For instance, [102] deal with the problem that the underlying dataset cannot be exposed, but good summaries may help the user undertake the task of data access.…”

“…These limitations impact the use of the retrieved data -machine learning can be unduly affected by the processing that was performed over a dataset prior to its release [125], while knowing the original purpose for collecting the data aids interpretation and analysis [140]. In other words, in a dataset search context, approaches need to consider additional aspects such as data provenance [27,53,64,87,101,142], annotations [67,93,144], quality [116,131,148], granularity of content [81], and schema [9,20] to effectively evaluate a dataset's fitness for a particular use. The user does not have the ability to introspect over large amounts of data, and their attention must be prioritized [13].…”

“…; DCAT [95] is the W3C standard for interoperability of catalogues, and contains a representation and vocabulary for datasets. Additional metadata, such as summarizations [81,106,144] could also be contributed. Unfortunately, the creation and maintenance of this metadata is currently resource intensive.…”