Attention, intentions, and the structure of discourse
Computational Linguistics
Intentions in the coordinated generation of graphics and text from tabular data
Knowledge and Information Systems
Saying It in Graphics: From Intentions to Visualizations
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Graph-based generation of referring expressions
Computational Linguistics
References to named entities: a corpus study
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Information graphics: an untapped resource for digital libraries
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A probabilistic framework for recognizing intention in information graphics
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The GREC challenge: overview and evaluation results
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
Recognizing the intended message of line graphs
Diagrams'10 Proceedings of the 6th international conference on Diagrammatic representation and inference
Automatically recognizing intended messages in grouped bar charts
Diagrams'12 Proceedings of the 7th international conference on Diagrammatic Representation and Inference
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Information graphics (such as bar charts and line graphs) are widely used in popular media. The majority of such non-pictorial graphics have the purpose of communicating a high-level message which is often not repeated in the text of the article. Thus, information graphics together with the textual segments contribute to the overall purpose of an article and cannot be ignored. Unfortunately, information graphics often do not label the dependent axis with a full descriptor of what is being measured. In order to realize the high-level message of an information graphic in natural language, a referring expression for the dependent axis must be generated. This task is complex in that the required referring expression often must be constructed by extracting and melding pieces of information from the textual content of the graphic. Our heuristic-based solution to this problem has been shown to produce reasonable text for simple bar charts. This paper presents the extensibility of that approach to other kinds of graphics, in particular to grouped bar charts and line graphs. We discuss the set of component texts contained in these two kinds of graphics, how the methodology for simple bar charts can be extended to these kinds, and the evaluation of the enhanced approach.