Hostname: page-component-89b8bd64d-ksp62 Total loading time: 0 Render date: 2026-05-07T03:17:34.720Z Has data issue: false hasContentIssue false
  • English
  • Français

Generating basic skills reports for low-skilled readers*

Published online by Cambridge University Press:  01 October 2008

SANDRA WILLIAMS  and
EHUD REITER
SANDRA WILLIAMS
Affiliation:
Department of Computing Science, The Open University, Milton Keynes MK7 6AA, UK e-mail: s.h.williams@open.ac.uk
EHUD REITER
Affiliation:
Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UKe.reiter@abdn.ac.uk
Get access Rights & Permissions [Opens in a new window]

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.

Information

Type
Papers
Information
Natural Language Engineering , Volume 14 , Issue 4 , October 2008 , pp. 495 - 525
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Bateman, John A. and Paris, Cécile L. (1989) Phrasing a text in terms the user can understand. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI, 1989, Detroit, MI, pp. 1511–17.Google Scholar
Binkley, M., Matheson, N. and Williams, T. (1997) Working Paper: Adult Literacy: An International Perspective. Technical Report, National Center for Education Statistics (NCES) Electronic Catalog No. NCES 9733, http://nces.ed.gov.Google Scholar
Brown, J. and Eskenazi, M. (2005) Student, text and curriculum modeling for reader-specific documant retrieval. In Proceedings of the IASTED International Conference on Human–Computer Interaction, Phoenix, AZ.Google Scholar
Canning, Y. (2002) Improved Syntactic Analysis of, and Simplified Text Generation from, Free-Form Text. PhD Thesis, University of Sunderland, Sunderland.Google Scholar
Carlson, L., Marcu, D. and Okurowski, M. E. (2003) Building a discourse-tagged corpus in the framework of rhetorical structure theory. In van Kuppevelt, Jan, and Smith, Ronnie (eds.), Current Directions in Discourse and Dialogue, Text, Speech and Language Technology, Vol. 22, pp. 85–112. Berlin, Springer.CrossRefGoogle Scholar
Chandrasekar, R. and Srinivas, B. (1997) Automatic induction of rules for text simplification. Knowledge-Based Systems 10: 183–90.CrossRefGoogle Scholar
Coleman, E. (1962) Improving comprehensibility by shortening sentences. Journal of Applied Psychology 46: 131–4.CrossRefGoogle Scholar
Collins-Thompson, K. and Callan, J. (2004) A language modeling approach to predicting reading difficulty. In Dumais, Susan, Marcu, Daniel and Roukos, Salim (eds.), HLT-NAACL 2004: Main Proceedings, pp. 193–200. Morristown, NJ: Association for Computational Linguistics.Google Scholar
Degand, L., Lefèvre, N. and Bestgen, Y. (1999) The impact of connectives and anaphoric expressions on expository discourse comprehension. Document Design 1: 39–51.CrossRefGoogle Scholar
Devlin, S., Canning, Y., Tait, J., Carroll, J., Minnen, G. and Pearce, D. (2000) An AAC aid for aphasic people with reading difficulties. In Proceedings of the 9th Biennial Conference of the International Society for Augmentative and Alternative Communication (ISAAC 2000), Washington, USA, pp. 10–12.Google Scholar
Devlin, S. and Tait, J. (1998) The use of a psycholinguistic database in the simplification of text for aphasic readers. In Nerbonne, John (ed.), Linguistic Databases, pp. 161–73. Cambridge: Cambridge University Press, CSLI Publications.Google Scholar
DeVries, H. (1999) Reading Ease@WWW. Masters Thesis, Macquarie University, Australia.Google Scholar
Di Eugenio, B., Glass, M., Trolio, M. J. and Haller, S. (2001) Simple natural language generation and intelligent tutoring systems. Proceedings of Artificial Intelligence in Education, pp. 50–8.Google Scholar
Di Eugenio, B., Moore, J. D. and Paolucci, M. (1997) Learning features that predict cue usage. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, Madrid, Spain, pp. 80–7.CrossRefGoogle Scholar
Eddy, B. (2002) Towards balancing conciseness, readability and salience: an integrated architecture. Proceedings of the International Natural Language Generation Conference, New York, pp. 173–8.Google Scholar
Geldof, S. (2003) Corpus analysis for NLG. In Reiter, E., Horacek, H. and van Deemter, K. (eds.), Proceedings of the 9th European Workshop on Natural Language Generation (ENLG'03), Budapest, Hungary, pp. 31–8.Google Scholar
Harley, T. (2001) The Psychology of Language from Data to Theory. Erlbaum, UK: Psychology Press.Google Scholar
Hunter, D. and Howard, U. (2004) Including language, literacy and numeracy learning in all post-16 education. Guidance on curriculum and methodology for generic initial teacher education programmes. Technical Report, FENTO (Further Education National Training Organisation), www.nrdc.org.uk.Google Scholar
Inui, K., Fujita, A., Takahashi, T., Iida, R. and Iwakura, T. (2003) Text simplification for reading assistance: a project note. 2nd International Conference on Paraphrasing: paraphrase acquisition and applications, Sapporo, Japan, pp. 9–16.CrossRefGoogle Scholar
Jucks, R. and Broome, R. (2007) Choice of words in doctor–patient communications: an analysis of health-related internet sites. Health Communication 21 (3): 267–77.CrossRefGoogle ScholarPubMed
Kintsch, W. and Vipond, D. (1979) Reading comprehension and readability in educational practice and psychological theory. In Nilsson, L. G. (ed.), Perspectives on Memory Research, pp. 329–65. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Knott, A. (1996) A Data-Driven Methodology for Motivating a Set of Coherence Relations. PhD Thesis, University of Edinburgh, Edinburgh.Google Scholar
Knott, A. and Sanders, T. (1998) The classification of coherence relations and their linguistic markers: an exploration of two languages. Journal of Pragmatics 30 (2): 135–75.CrossRefGoogle Scholar
Lavoie, B. and Rambow, O. (1997) RealPro: A fast, portable sentence realizer. IN Proceedings of the Conference on Applied Natural Language Processing (ANLP, 1997), Washington, USA, pp. 265–8.CrossRefGoogle Scholar
Leijten, M. and van Waes, L. (2001) The impact of text structure and linguistic markers on the text comprehension of elderly people. In Degand, L., Bestgen, Y., Spooren, W. and van Waes, L. (eds.), Multidisciplinary Approaches to Discourse, pp. 21–9. Amsterdam: Stichting Neerlandistiek VU, Münster: Nodus Publikationen.Google Scholar
Lorch, R. F. and Lorch, E. P. (1996) Effects of organizational signals on free recall of expository text. Journal of Educational Psychology 88 (1): 38–48.CrossRefGoogle Scholar
Mann, W. C. and Thompson, S. A. (1987) Rhetorical structure theory: a theory of text organization. Technical Report, ISI/RS-87-190, Document Center, USC/ISI, Marina del Rey, CA.Google Scholar
Mason, J. and Morris, L. (2000) Improving understanding and recall of the probation service contract. Journal of Community and Applied Social Psychology 10 (3): 199–210.3.0.CO;2-9>CrossRefGoogle Scholar
McKeown, K., Robin, J. and Tanenblatt, M. (1993) Tailoring lexical choice to the user's vocabulary in multimedia explanation generation. In Proceedings of ACL, Columbus, OH, pp. 226–34.Google Scholar
Milosavljevic, M. and Oberlander, J. (1998) Dynamic hypertext catalogues: helping users to help themselves. In Proceedings of the 9th ACM Conference on Hypertext and Hypermedia (HT, 1998), Pittsburgh, PA, pp. 123–31.CrossRefGoogle Scholar
Miltsakaki, E., Dinesh, N., Prasad, R., Joshi, A. and Webber, B. (2005) Experiments on sense annotations and sense disambiguation of discourse connectives. In Proceedings of the 4th Workshop on Treebanks and Linguistic Theories, Barcelona, Spain.Google Scholar
Moore, J. D., Porayska-Pomsta, K., Varges, S. and Zinn, C. (2004) Generating tutorial feedback with affect. In Proceedings of the 17th International Florida Artificial Intelligence Research Society Conference, Miami Beach, FL, pp. 923–8. AAAI Press, Menlo Park, CA.Google Scholar
Moser, C. (1999) Improving literacy and numeracy: a fresh start. The report of the working group chaired by Sir Claus Moser. Technical Report, www.lifelonglearning.co.uk/mosergroup.Google Scholar
Moser, M. and Moore, J. D. (1995) Investigating cue selection and placement in tutorial discourse. In Proceedings of the 33rd Annual Meeting on Association For Computational Linguistics (Cambridge, Massachusetts, June 26–30, 1995). Annual Meeting of the ACL. Association for Computational Linguistics, Morristown, NJ, USA, 130–135.Google Scholar
Okumura, M. (2000) Producing more readable extracts by revising them. COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics, Saarbrücken, Germany, pp. 1071–5.Google Scholar
Paris, Cécile L. (1988) Tailoring object descriptions to the user's level of expertise. Computational Linguistics 14 (3): 64–78.Google Scholar
Power, R. (2000) Planning texts by constraint satisfaction. In Proceedings of the International Conference on Computational Linguistics (COLING, 2000), Saarbrücken, Germany, pp. 642–8.CrossRefGoogle Scholar
Power, R., Scott, D. and Bouayad-Agha, N. (2003) Document structure. Computational Linguistics 29 (2): 211–60.Google Scholar
Reiter, E. and Dale, R. (2000) Building Natural Language Generation Systems. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Reiter, E. and Sripada, S. G. (2002) Human variation and lexical choice. Computational Linguistics 28: 545–53.CrossRefGoogle Scholar
Reiter, E., Robertson, R and Osman, L. M. (2003) Lessons from a failure: Generating tailored smoking cessation letters. Artificial Intelligence, 144 (1–2): 41–58.Google Scholar
Reiter, E., Sripada, S. G. and Robertson, R. (2003) Acquiring correct knowledge for natural anguage generation. Journal of Artificial Intelligence Research 18: 491–516.Google Scholar
Reiter, E., Williams, S. and Crichton, L. (2005) Generating feedback reports for adults taking basic skills tests. In Proceedings of the Twenty-fifth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, pp. 50–63.Google Scholar
Sanders, T. J. M. and Noordman, L. G. M. (2000) The role of coherence relations and their linguistic markers in text processing. Discourse Processes 29 (1): 37–60.CrossRefGoogle Scholar
Scott, D. and de Souza, C. (1990) Getting the message across in RST-based text generation. In Dale, R., Mellish, C. and Zock, M. (eds.), Current Research in Natural Language Generation, pp. 47–73. Cognitive Science Series. London: Academic Press.Google Scholar
Siddharthan, A. (2002) Resolving attachment and clause boundary ambiguities for simplifying relative clause constructs. In Proceedings of the Student Research Workshop, 40th Meeting of the Association for Computational Linguistics, Philadelphia, PA, pp. 60–5.Google Scholar
Siddharthan, A. (2003) Preserving discourse structure when simplifying text. Proceedings of the 9th European Workshop on Natural Language Generation, Budapest, Hungary, pp. 127–34.Google Scholar
Steeds, A. (2001) Adult literacy core curriculum including spoken communication. Technical Report, Cambridge Training and Development Ltd. on behalf of The Basic Skills Agency, ISBN 1-85990-127-1.Google Scholar
Tintarev, N. (2004) Content Determination for Reports Aimed at Adult Literacy Learners. Masters Thesis, Uppsala Universitet, Sweden.Google Scholar
Torrens, M. (2002) Java constraint library 2.1. Technical Report, Artificial Intelligence Laboratory, Swiss Federal Institute of Technology.Google Scholar
Walker, M., Whittaker, S., Stent, A., Maloor, P., Moore, J., Johnston, M. and Vasireddy, G. (2003) Generation and evaluation of user tailored responses in multimodal dialogue. Cognitive Science, Rumelhart Prize Special Issue Honoring Aravind K. Joshi, 28 (5): 811–40.Google Scholar
Williams, S. (2004) Natural Language Generation of Discourse Relations for Different Reading Levels. PhD Thesis, University of Aberdeen, Aberdeen.Google Scholar
Williams, S. and Reiter, E. (2005) Deriving content selection rules from a corpus of non-naturally occurring documents for a novel NLG application. In Proceedings of the Workshop on Using Corpora for Natural Language Generation, pp. 41–8. Technical Report, no. ITRI–05–03, University of Brighton: Information Technology Research Institute (ITRI).Google Scholar
Zukerman, I. and Pearl, J. (1986) Comprehension-driven generation of meta-technical utterances in math tutoring. In 5th National Conference AAAI-86, Philadelphia, PA, pp. 606–11.Google Scholar