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Approaches to Large-Scale User Opinion Summarization for the Web
Abstract
This chapter discusses approaches to applying text summarization research to the real-world problem of opinion summarization of user comments. Following a brief overview of the history of research in text summarization, the authors consider large scale user opinion summarization on the Web, a summarization problem that is distinct from the traditional domain that the research has focused on until very recently. More specifically, they consider opinion summarization of large datasets that generally include large degrees of noise and little editorial structure. To deal with this kind of real-world problem, the chapter addresses three major areas that must be considered and adhered to when designing systems for this type of problem: simple techniques, domain knowledge, and evaluative testing. Each area is covered in detail, and throughout the chapter, the lessons are applied to a case study that aims to apply the recommendations to designing a real-world opinion summarization system for a fictional book publisher.
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