Query-Based Document Skimming: A User-Centred Evaluation of Relevance Profiling

Abstract

We present a user-centred, task-oriented, comparative evaluation of two query-based document skimming tools. ProfileSkim bases within-document retrieval on computing a relevance profile for a document and query; FindSkim provides similar functionality to the web browser Find-command. A novel simulated work task was devised, where experiment participants are asked to identify (index) relevant pages of an electronic book, given subjects from the existing book index. This subject index provides the ground truth, against which the indexing results can be compared. Our major hypothesis was confirmed, namely ProfileSkim proved significantly more efficient than Find-Skim, as measured by time for task. Moreover, indexing task effectiveness, measured by typical IR measures, demonstrated that ProfileSkim was better than FindSkim in identifying relevant pages, although not significantly so. The experiments confirm the potential of relevance profiling to improve query-based document skimming, which should prove highly beneficial for users trying to identify relevant information within long documents.