1,164 research outputs found
Exploranative Code Quality Documents
Good code quality is a prerequisite for efficiently developing maintainable
software. In this paper, we present a novel approach to generate exploranative
(explanatory and exploratory) data-driven documents that report code quality in
an interactive, exploratory environment. We employ a template-based natural
language generation method to create textual explanations about the code
quality, dependent on data from software metrics. The interactive document is
enriched by different kinds of visualization, including parallel coordinates
plots and scatterplots for data exploration and graphics embedded into text. We
devise an interaction model that allows users to explore code quality with
consistent linking between text and visualizations; through integrated
explanatory text, users are taught background knowledge about code quality
aspects. Our approach to interactive documents was developed in a design study
process that included software engineering and visual analytics experts.
Although the solution is specific to the software engineering scenario, we
discuss how the concept could generalize to multivariate data and report
lessons learned in a broader scope.Comment: IEEE VIS VAST 201
Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards
[EN]Information dashboards are everywhere. They support knowledge discovery in a huge
variety of contexts and domains. Although powerful, these tools can be complex, not only for the
end-users but also for developers and designers. Information dashboards encode complex datasets
into different visual marks to ease knowledge discovery. Choosing a wrong design could
compromise the entire dashboard’s effectiveness, selecting the appropriate encoding or
configuration for each potential context, user, or data domain is a crucial task. For these reasons,
there is a necessity to automatize the recommendation of visualizations and dashboard
configurations to deliver tools adapted to their context. Recommendations can be based on different
aspects, such as user characteristics, the data domain, or the goals and tasks that will be achieved or
carried out through the visualizations. This work presents a dashboard meta-model that abstracts
all these factors and the integration of a visualization task taxonomy to account for the different
actions that can be performed with information dashboards. This meta-model has been used to
design a domain specific language to specify dashboards requirements in a structured way. The
ultimate goal is to obtain a dashboard generation pipeline to deliver dashboards adapted to any
context, such as the educational context, in which a lot of data are generated, and there are several
actors involved (students, teachers, managers, etc.) that would want to reach different insights
regarding their learning performance or learning methodologies
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization
Good figure captions help paper readers understand complex scientific
figures. Unfortunately, even published papers often have poorly written
captions. Automatic caption generation could aid paper writers by providing
good starting captions that can be refined for better quality. Prior work often
treated figure caption generation as a vision-to-language task. In this paper,
we show that it can be more effectively tackled as a text summarization task in
scientific documents. We fine-tuned PEGASUS, a pre-trained abstractive
summarization model, to specifically summarize figure-referencing paragraphs
(e.g., "Figure 3 shows...") into figure captions. Experiments on large-scale
arXiv figures show that our method outperforms prior vision methods in both
automatic and human evaluations. We further conducted an in-depth investigation
focused on two key challenges: (i) the common presence of low-quality
author-written captions and (ii) the lack of clear standards for good captions.
Our code and data are available at:
https://github.com/Crowd-AI-Lab/Generating-Figure-Captions-as-a-Text-Summarization-Task.Comment: Accepted by INLG-202
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