Deriving content selection rules from a corpus of non-naturally occurring documents for a novel NLG application

We describe a methodology for deriving content selection rules for NLG applications that aim to replace oral communications from human experts by written communications that are generated automatically. We argue for greater involvement of users and for a strategy for handling sparse data

A corpus analysis of discourse relations for Natural Language Generation

We are developing a Natural Language Generation (NLG) system that generates texts tailored for the reading ability of individual readers. As part of building the system, GIRL (Generator for Individual Reading Levels), we carried out an analysis of the RST Discourse Treebank Corpus to find out how human writers linguistically realise discourse relations. The goal of the analysis was (a) to create a model of the choices that need to be made when realising discourse relations, and (b) to understand how these choices were typically made for “normal” readers, for a variety of discourse relations. We present our results for discourse relations: concession, condition, elaboration additional, evaluation, example, reason and restatement. We discuss the results and how they were used in GIRL

Generating Feedback Reports for Adults Taking Basic Skills Tests

SkillSum is an Artificial Intelligence (AI) and Natural Language Generation (NLG) system that produces short feedback reports for people who are taking online tests which check their basic literacy and numeracy skills. In this paper, we describe the SkillSum system and application, focusing on three challenges which we believe are important ones for many systems which try to generate feedback reports from Web-based tests: choosing content based on very limited data, generating appropriate texts for people with varied levels of literacy and knowledge, and integrating the web-based system with existing assessment and support procedures

Tailored Patient Information: Some Issues and Questions

Tailored patient information (TPI) systems are computer programs which produce personalised heath-information material for patients. TPI systems are of growing interest to the natural-language generation (NLG) community; many TPI systems have also been developed in the medical community, usually with mail-merge technology. No matter what technology is used, experience shows that it is not easy to field a TPI system, even if it is shown to be effective in clinical trials. In this paper we discuss some of the difficulties in fielding TPI systems. This is based on our experiences with 2 TPI systems, one for generating asthma-information booklets and one for generating smoking-cessation letters.Comment: This is a paper about technology-transfer. It does not have much technical conten

Reading errors made by skilled and unskilled readers: evaluating a system that generates reports for people with poor literacy

This study evaluates a natural language generation system that creates literacy assessment reports in order to create more readable documents. Prior research assessed comprehension and reading speed on modified documents. Here, we investigate whether individuals make less reading errors after the system modifies documents to make them more readable. Preliminary results show that poor readers make more errors than good readers. The full paper will describe readers' rates of errors on documents modified for readability

Appropriate Microplanning Choices for Low-Skilled Readers

We have developed a set of microplanning choice rules which are intended to enable Natural Language Generation (NLG) systems to generate appropriate texts for readers with below-average literacy, focusing in particular on choices related to how discourse structure is expressed (cue phrases, ordering, sentence structure). Evaluation experiments suggest that our rules do enhance the readability of texts for low-skilled readers, although there is still room for improvement