8. Conclusions and Future Work
Captions that explain novel or creative graphics can be crucial in understanding how data and various relations are expressed in them. This paper presents a framework for generating explanatory captions for information graphics. The system generates captions based on: (1) a representation of the structure of the graphical presentation and its mapping to the data it depicts, (2) a framework for identifying the perceptual complexity of graphical elements, and (3) the structure of the data expressed in the graphic.
One of the strengths of our approach is that the system is able to generate surprisingly effective and comprehensible descriptions in the absence of a detailed semantic model for the domain. The captions shown in this document were generated using only the data characterization used by sage for designing the visual presentation and an extremely basic lexical representation. Thus, the caption generation mechanism can be quickly and easily transferred to another domain (the only thing required is a lexicon for the new terms). However, this is also a limitation, because under certain circumstances, the system generates seemingly odd descriptions. This occurs in cases where the underlying database representation happens to contain attribute specifications that differ from the way they would normally be described in discourse. For instance, if the database schema happened to relate house attributes such as house address, number of rooms and sale price to the (owner) of the house, rather than the house itself, the system would generate statements such as "John's sale price is "
A secondary limitation of our implementation is that it does not generate general graphical annotations. While the system can (and does) highlight specific graphemes in the presentation if so required by the planner (currently done to single out the tuple being used in an example), the system does not coordinate the generation of graphical keys and the captions. This is because our speech act language does not permit bi-directional communication between the text planner and sage . The ability to specify arbitrary graphical annotations in the speech act language would make the current simple specification quite complex. As we extend the planning framework to generate both the text and the graphics, this will be remedied as well.
There are two ways to facilitate an effective use of a graphic: (1) explaining how the graphic expresses its data, and (2) conveying what aspects of the data are relevant to the current user's analysis task. In the work described in this paper, we have addressed the first issue. We are currently working on the second one.
To next section.
Paper Sections:To Title page
To Part 1: Introduction
To Part 2: SAGE: A System for Automatic Graphical Explanations
To Part 3: Discourse Strategies for Generating Captions
To Part 4: Graphical Complexity: The Need for Clarification
To Part 5: Generating Explanatory Captions
To Part 6: System Implementation and Evaluation
To Part 7: Related Work
To Appendix A
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