Generation of extended bilingual statistical reports

Iordanskaja, Lidija; Kim, M.; Kittredge, Richard; Lavoie, Benoit; Polguère, Alain

doi:10.3115/993079.993120

Cited by 29 publications

(9 citation statements)

References 4 publications

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“…This was probably the first demonstration of this kind for a data-to-text NLG system. A number of other data-to-text systems have been developed to summarise small data sets, including summaries of statistical data [24,41], air quality reports [6], and financial data [18,46].…”

Section: Data-to-text Systemsmentioning

confidence: 99%

Automatic generation of textual summaries from neonatal intensive care data

Portet¹,

Reiter²,

Gatt³

et al. 2009

Artificial Intelligence

187

131

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Effective presentation of data for decision support is a major issue when large volumes\ud of data are generated as happens in the Intensive Care Unit (ICU). Although the most\ud common approach is to present the data graphically, it has been shown that textual\ud summarisation can lead to improved decision making. As part of the BabyTalk project,\ud we present a prototype, called BT-45, which generates textual summaries of about 45\ud minutes of continuous physiological signals and discrete events (e.g.: equipment settings\ud and drug administration). Its architecture brings together techniques from the different\ud areas of signal processing, medical reasoning, knowledge engineering, and natural language\ud generation. A clinical off-ward experiment in a Neonatal ICU (NICU) showed that human\ud expert textual descriptions of NICU data lead to better decision making than classical\ud graphical visualisation, whereas texts generated by BT-45 lead to similar quality decisionmaking\ud as visualisations. Textual analysis showed that BT-45 texts were inferior to human\ud expert texts in a number of ways, including not reporting temporal information as well\ud and not producing good narratives. Despite these deficiencies, our work shows that it\ud is possible for computer systems to generate effective textual summaries of complex\ud continuous and discrete temporal clinical data.peer-reviewe

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Section: Data-to-text Systemsmentioning

confidence: 99%

Automatic generation of textual summaries from neonatal intensive care data

Portet¹,

Reiter²,

Gatt³

et al. 2009

Artificial Intelligence

187

131

View full text Add to dashboard Cite

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“…Some systems have focused on report generation, namely ANA by Karen Kukich (1983), which generates stock market reports directly from the Dow Jones indicators. LFS (Iordanskaja, et al, 1992) produces reports in both French and English on the active population of Canada; it has also been adapted for generating reports on detail sales and consumer report price index. It can generate good reports in the style expected from a government agency.…”

Section: Automatic Text Generationmentioning

confidence: 99%

Intentions in the Coordinated Generation of Graphics and Text from Tabular Data

Fasciano¹,

Lapalme²

2000

Knowledge and Information Systems

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To use graphics efficiently in an automatic report generation system, one has to model messages and how they go from the writer (intention) to the reader (interpretation). This paper describes PostGraphe, a system which generates a report integrating graphics and text from a set of writer's intentions. The system is given the data in tabular form as might be found in a spreadsheet; also input is a declaration of the types of values in the columns of the table. The user then indicates the intentions to be conveyed in the graphics (e.g., compare two variables or show the evolution of a set of variables) and the system generates a report in L A T E X with the appropriate PostScript graphic files. PostGraphe uses the same information to generate the accompanying text that helps the reader to focus on the important points of the graphics. We also describe how these ideas have been embedded to create a new Chart Wizard for Microsoft Excel.

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“…For example, BabyTalk (Portet et al, 2007) generates medical reports from sensors monitoring a baby in a Neonatal Intensive Care Unit, while (Hallett and Scott, 2005) describe a system for generating reports from events in medical records. SumTime , (Coch, 1998) and Fog (Goldberg et al, 1994) generate weather forecasts from the output of weather computer simulation models, while (Iordanskaja et al, 1992) and (Rösner, 1987) both generate summaries from employment statistics.…”

Section: Introductionmentioning

confidence: 99%

Using spatial reference frames to generate grounded textual summaries of georeferenced data

Turner

Sripada

Reiter

et al. 2008

Proceedings of the Fifth International Natural Language Generation Conference on - INLG '08

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Summarising georeferenced (can be identified according to it's location) data in natural language is challenging because it requires linking events describing its nongeographic attributes to their underlying geography. This mapping is not straightforward as often the only explicit geographic information such data contains is latitude and longitude. In this paper we present an approach to generating textual summaries of georeferenced data based on spatial reference frames. This approach has been implemented in a data-to-text system we have deployed in the weather forecasting domain.

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Generation of extended bilingual statistical reports

Cited by 29 publications

References 4 publications

Automatic generation of textual summaries from neonatal intensive care data

Automatic generation of textual summaries from neonatal intensive care data

Intentions in the Coordinated Generation of Graphics and Text from Tabular Data

Using spatial reference frames to generate grounded textual summaries of georeferenced data

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