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

Query-Based Summarization using Rhetorical Structure Theory

Research on Question Answering is focused mainly on classifying the question type and finding the answer. Presenting the answer in a way that suits the user’s needs has received little attention. This paper shows how existing question answering systems—which aim at finding precise answers to questions—can be improved by exploiting summarization techniques to extract more than just the answer from the document in which the answer resides. This is done using a graph search algorithm which searches for relevant sentences in the discourse structure, which is represented as a graph. The Rhetorical Structure Theory (RST) is used to create a graph representation of a text document. The output is an extensive answer, which not only answers the question, but also gives the user an opportunity to assess the accuracy of the answer (is this what I am looking for?), and to find additional information that is related to the question, and which may satisfy an information need. This has been implemented in a working multimodal question answering system where it operates with two independently developed question answering modules