@article {17397,
	title = {Temporal Summaries: Supporting Temporal Categorical Searching, Aggregation and Comparison},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {15},
	year = {2009},
	month = {2009/12//Nov},
	pages = {1049 - 1056},
	abstract = {When analyzing thousands of event histories, analysts often want to see the events as an aggregate to detect insights and generate new hypotheses about the data. An analysis tool must emphasize both the prevalence and the temporal ordering of these events. Additionally, the analysis tool must also support flexible comparisons to allow analysts to gather visual evidence. In a previous work, we introduced align, rank, and filter (ARF) to accentuate temporal ordering. In this paper, we present temporal summaries, an interactive visualization technique that highlights the prevalence of event occurrences. Temporal summaries dynamically aggregate events in multiple granularities (year, month, week, day, hour, etc.) for the purpose of spotting trends over time and comparing several groups of records. They provide affordances for analysts to perform temporal range filters. We demonstrate the applicability of this approach in two extensive case studies with analysts who applied temporal summaries to search, filter, and look for patterns in electronic health records and academic records.},
	keywords = {Aggregates, Collaborative work, Computational Biology, Computer Graphics, Data analysis, data visualisation, Data visualization, Databases, Factual, Displays, Event detection, Filters, Heparin, History, Human computer interaction, Human-computer interaction, HUMANS, Information Visualization, Interaction design, interactive visualization technique, Medical Records Systems, Computerized, Pattern Recognition, Automated, Performance analysis, Springs, temporal categorical data visualization, temporal categorical searching, temporal ordering, temporal summaries, Thrombocytopenia, Time factors},
	isbn = {1077-2626},
	doi = {10.1109/TVCG.2009.187},
	author = {Wang,T. D and Plaisant, Catherine and Shneiderman, Ben and Spring, Neil and Roseman,D. and Marchand,G. and Mukherjee,V. and Smith,M.}
}
@article {13770,
	title = {Exploiting aspectual features and connecting words for summarization-inspired temporal-relation extraction},
	journal = {Information Processing \& Management},
	volume = {43},
	year = {2007},
	month = {2007/11//},
	pages = {1681 - 1704},
	abstract = {This paper presents a model that incorporates contemporary theories of tense and aspect and develops a new framework for extracting temporal relations between two sentence-internal events, given their tense, aspect, and a temporal connecting word relating the two events. A linguistic constraint on event combination has been implemented to detect incorrect parser analyses and potentially apply syntactic reanalysis or semantic reinterpretation{\textemdash}in preparation for subsequent processing for multi-document summarization. An important contribution of this work is the extension of two different existing theoretical frameworks{\textemdash}Hornstein{\textquoteright}s 1990 theory of tense analysis and Allen{\textquoteright}s 1984 theory on event ordering{\textemdash}and the combination of both into a unified system for representing and constraining combinations of different event types (points, closed intervals, and open-ended intervals). We show that our theoretical results have been verified in a large-scale corpus analysis. The framework is designed to inform a temporally motivated sentence-ordering module in an implemented multi-document summarization system.},
	keywords = {Parsing and corpus analysis, Summarization, temporal ordering, Temporal-relation extraction, Tense, aspect and connecting words},
	isbn = {0306-4573},
	doi = {10.1016/j.ipm.2007.01.008},
	url = {http://www.sciencedirect.com/science/article/pii/S0306457307000271},
	author = {Dorr, Bonnie J and Gaasterland,Terry}
}
