The automatic generation of narratives

We present the Narrator, a Natural Language Generation component used in a digital storytelling system. The system takes as input a formal representation of a story plot, in the form of a causal network relating the actions of the characters to their motives and their consequences. Based on this input, the Narrator generates a narrative in Dutch, by carrying out tasks such as constructing a Document Plan, performing aggregation and ellipsis and the generation of appropriate referring expressions. We describe how these tasks are performed and illustrate the process with examples, showing how this results in the generation of coherent and well-formed narrative texts

Generating ellipsis using discourse structures

This article describes an effort to generate elliptic sentences, using Dependency Trees connected by Discourse Relations as input. We contend that the process of syntactic aggregation should be performed in the Surface Realization stage of the language generation process, and that Dependency Trees with Rhetorical Relations are excellent input for a generation system that has to generate ellipsis. We also propose a taxonomy of the most common Dutch cue words, grouped according to the kind of discourse relations they signal

Cost-based attribute selection for GRE (GRAPH-SC/GRAPH-FP)

In this paper we discuss several approaches to the problem of content determination for the generation of referring expressions (GRE) using the Graphbased framework of Krahmer et al. (2003). This work was carried out in the context of the First NLG Shared Task and Evaluation Challenge on Attribute Selection for Referring Expression Generation

Normalized Alignment of Dependency Trees for Detecting Textual Entailment

In this paper, we investigate the usefulness of normalized alignment of dependency trees for entailment prediction. Overall, our approach yields an accuracy of 60% on the RTE2 test set, which is a significant improvement over the baseline. Results vary substantially across the different subsets, with a peak performance on the summarization data. We conclude that normalized alignment is useful for detecting textual entailments, but a robust approach will probably need to include additional sources of information

The AISB’08 Symposium on Multimodal Output Generation (MOG 2008)

Welcome to Aberdeen at the Symposium on Multimodal Output Generation (MOG 2008)! In this volume the papers presented at the MOG 2008 international symposium are collected

System demonstration goalgetter: generation of spoken soccer reports

In this paper we describe a demonstration of the GoalGetter system, which generates spoken soc- cer reports (in Dutch) on the basis of tabular data. Two types of speech output are available. The demo runs via the web. It includes the possibility of !creating your own match' and having GoaiGetter generate a report on this match. . ,_ 1. About the system The GoalGetter system is a Data-to-Speech system which generates spoken soccer reports (in Dutch) on the basis of tabular data. The system takes as input data about a soccer match that are derived from a Teletext page. 1 The output of the system is a spoken, natural language report conveying the main events of the match 'described on the Teletext page

Illustrating answers: an evaluation of automatically retrieved illustrations of answers to medical questions

In this paper we discuss and evaluate a method for automatic text illustration, applied to answers to medical questions. Our method for selecting illustrations is based on the idea that similarities between the answers and picture-related text (the picture’s caption or the section/paragraph that includes the picture) can be used as evidence that the picture would be appropriate to illustrate the answer.In a user study, participants rated answer presentations consisting of a textual component and a picture. The textual component was a manually written reference answer; the picture was automatically retrieved by measuring the similarity between the text and either the picture’s caption or its section. The caption-based selection method resulted in more attractive presentations than the section-based method; the caption-based method was also more consistent in selecting informative pictures and showed a greater correlation between user-rated informativeness and the confidence of relevance of the system.When compared to manually selected pictures, we found that automatically selected pictures were rated similarly to decorative pictures, but worse than informative pictures