How to talk to strangers: Generating medical reports for first-time users

Gkatzia, Dimitra; Rieser, Verena; Lemon, Oliver

doi:10.1109/fuzz-ieee.2016.7737739

Cited by 4 publications

(7 citation statements)

References 34 publications

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“…30 minutes can be described as "time taken for lunch". Gkatzia et al [24] propose an approach to handling unknown first-time users in the context of automatic report generation from time-series data in the health domain. For this task, the uncertain factor is the users and their preferences to which the output needs to be adapted.…”

Section: Power and Williamsmentioning

confidence: 99%

Data-to-Text Generation Improves Decision-Making Under Uncertainty

Gkatzia

Lemon

Rieser

2017

IEEE Comput. Intell. Mag.

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Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilities. This article presents a comparison of different information presentations for uncertain data and, for the first time, measures their effects on human decision-making, in the domain of weather forecast generation. We use a game-based setup to evaluate the different systems. We show that the use of Natural Language Generation (NLG) enhances decision-making under uncertainty, compared to state-of-the-art graphical-based representation methods. In a task-based study with 442 adults, we found that presentations using NLG led to 24% better decision-making on average than the graphical presentations, and to 44% better decision-making when NLG is combined with graphics. We also show that women achieve significantly better results when presented with NLG output (an 87% increase on average compared to graphical presentations). Finally, we present a further analysis of demographic data and its impact on decision-making, and we discuss implications for future NLG systems.

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Section: Power and Williamsmentioning

confidence: 99%

Data-to-Text Generation Improves Decision-Making Under Uncertainty

Gkatzia

Lemon

Rieser

2017

IEEE Comput. Intell. Mag.

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“…Data-to-text systems, systems that "generate texts from non-linguistic data, such as sensor data and event logs" (Reiter, 2007, p. 97), have been around for a long time and still remain a popular topic for Natural Language Generation. Some of the datato-text language generation tasks that have been investigated recently include weather forecast generation (Belz and Kow, 2010;Angeli et al, 2010;Gkatzia et al, 2016a, among others), medical reports (Gatt et al, 2009;Gkatzia et al, 2016b;Schneider et al, 2013, among others), and financial reports (Nesterenko, 2016, among others).…”

Section: Related Workmentioning

confidence: 99%

“…Some of the datato-text language generation tasks that have been investigated recently include weather forecast generation (Belz and Kow, 2010;Angeli et al, 2010;Gkatzia et al, 2016a, among others), medical reports (Gatt et al, 2009;Gkatzia et al, 2016b;Schneider et al, 2013, among others), and financial reports (Nesterenko, 2016, among others).…”

Section: Related Workmentioning

confidence: 99%

PASS: A Dutch data-to-text system for soccer, targeted towards specific audiences

Lee¹,

Krahmer²,

Wubben³

2017

Proceedings of the 10th International Conference on Natural Language Generation

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We present PASS, a data-to-text system that generates Dutch soccer reports from match statistics. One of the novel elements of PASS is the fact that the system produces corpusbased texts tailored towards fans of one club or the other, which can most prominently be observed in the tone of voice used in the reports. Furthermore, the system is open source and uses a modular design, which makes it relatively easy for people to add extensions. Human-based evaluation shows that people are generally positive towards PASS in regards to its clarity and fluency, and that the tailoring is accurately recognized in most cases.

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“…For example, an NLG system can read sensor data and produce a comprehensive textual summary. Previous research on generation from time-series data has been conducted in several domains such as weather forecasts (Sripada et al, 2004;Konstas and Lapata, 2012;Gkatzia et al, 2016a), health informatics (Gatt et al, 2009;Gkatzia et al, 2016b), stock market summaries (Kukich, 1983) and assistive technology systems (Black et al, 2010). These systems have employed different content selection methods, which are reviewed in Section 4 and 5.…”

Section: Achieving the Communicative Goalmentioning

confidence: 99%

“…This issue has been raised by several researchers, such as (Janarthanam, 2011;Han et al, 2014), to name a few. Previous approaches to tackling this issue include the use of latent User Models (Han et al, 2014), initial questionnaires to derive information by the user (Reiter et al, 1999) and tackling first-time users using multi-objective optimisation (Gkatzia et al, 2016b).…”

Section: Challenges For Content Selection Inmentioning

confidence: 99%

Content Selection in Data-to-Text Systems: A Survey

Gkatzia¹

2016

Preprint

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Data-to-text systems are powerful in generating reports from data automatically and thus they simplify the presentation of complex data. Rather than presenting data using visualisation techniques, datato-text systems use natural (human) language, which is the most common way for human-human communication. In addition, data-to-text systems can adapt their output content to users' preferences, background or interests and therefore they can be pleasant for users to interact with. Content selection is an important part of every data-to-text system, because it is the module that determines which from the available information should be conveyed to the user. This survey initially introduces the field of data-to-text generation, describes the general data-to-text system architecture and then it reviews the state-ofthe-art content selection methods. Finally, it provides recommendations for choosing an approach and discusses opportunities for future research.

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How to talk to strangers: Generating medical reports for first-time users

Cited by 4 publications

References 34 publications

Data-to-Text Generation Improves Decision-Making Under Uncertainty

Data-to-Text Generation Improves Decision-Making Under Uncertainty

PASS: A Dutch data-to-text system for soccer, targeted towards specific audiences

Content Selection in Data-to-Text Systems: A Survey

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