The Open UniversitySkip to content
 

Generating basic skills reports for low-skilled readers

Williams, Sandra and Reiter, Ehud (2008). Generating basic skills reports for low-skilled readers. Natural Language Engineering, 14(4) pp. 495–525.

Full text available as:
[img]
Preview
PDF (Not Set) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (836Kb)
DOI (Digital Object Identifier) Link: http://doi.org/10.1017/S1351324908004725
Google Scholar: Look up in Google Scholar

Abstract

We describe SkillSum, a Natural Language Generation (NLG) system that generates a personalised feedback report for someone who has just completed a screening assessment of their basic literacy and numeracy skills. Because many SkillSum users have limited literacy, the generated reports must be easily comprehended by people with limited reading skills; this is the most novel aspect of SkillSum, and the focus of this paper. We used two approaches to maximise readability. First, for determining content and structure (document planning), we did not explicitly model readability, but rather followed a pragmatic approach of repeatedly revising content and structure following pilot experiments and interviews with domain experts. Second, for choosing linguistic expressions (microplanning), we attempted to formulate explicitly the choices that enhanced readability, using a constraints approach and preference rules; our constraints were based on corpus analysis and our preference rules were based on psycholinguistic findings. Evaluation of the SkillSum system was twofold: it compared the usefulness of NLG technology to that of canned text output, and it assessed the effectiveness of the readability model. Results showed that NLG was more effective than canned text at enhancing users' knowledge of their skills, and also suggested that the empirical 'revise based on experiments and interviews' approach made a substantial contribution to readability as well as our explicit psycholinguistically inspired models of readability choices.

Item Type: Journal Article
ISSN: 1351-3249
Academic Unit/Department: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 12441
Depositing User: Sandra Williams
Date Deposited: 26 Nov 2008 11:32
Last Modified: 04 Oct 2016 11:38
URI: http://oro.open.ac.uk/id/eprint/12441
Share this page:

Altmetrics

Scopus Citations

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

▼ Automated document suggestions from open access sources

Actions (login may be required)

Policies | Disclaimer

© The Open University   + 44 (0)870 333 4340   general-enquiries@open.ac.uk