Combining Low-Level and Summary Representations of Opinions for Multi-Perspective Question Answering

While much recent progress has been made in research on fact-based question answering, our work aims to extend question-answering research in a different direction — to handle multi-perspective question-answering tasks, i.e. question-answering tasks that require an ability to find and organize opinions in text. In particular, this paper proposes an approach to multi-perspective question answering that views the task as one of opinion-oriented information extraction. We first describe an annotation scheme developed for the low-level representation of opinions, and note the results of interannotator agreement studies using the opinion-based annotation framework. Next, we propose the use of opinion-oriented “scenario templates” to act as a summary representation of the opinions expressed in a document, a set of documents, or an arbitrary text segment. Finally, we outline an approach for the automatic construction of opinion-based summary representations and describe how they might be used to support a variety of multi-perspective question answering tasks.