Generating interactive multimedia summaries of evaluative text.

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Many organizations and individuals are faced daily with the challenge of managing large corpora of text data. One important application is evaluative text, i.e. any document expressing an evaluation of an entity as either positive or negative. Evaluative information is strategic to decision making.  For instance, many websites collect large quantities of online customer reviews of consumer electronics. Although this literature can be of great strategic value to product designers, planners and manufacturers, the effective processing of this information remains a complex and time-consuming task. As another example, before traveling abroad, many users browse through travel-logs and online user reviews on hotels before they make reservations. However, there are so many evaluation web sites that users are awash in reviews.In these and many other similar situations, an automated solution has the potential to greatly reduce both the cost and time necessary to keep up with the growing amount of evaluative literature. Instead of asking the user to read through one review at a time, we propose to summarize the text reviews faithfully in a multi-granularity fashion, so that the user can pick the appropriate granularity level depending on her needs and time constraints.While there has been substantial work on how evaluative information can be extracted from text, little attention has been paid to how the extracted information can be effectively summarized and presented to users. In previous work we have started to explore this problem and in this project we propose to extend our work in different directions. In general, we aim at removing simplifying assumptions, at increasing the sophistication of the summarization process input, and at improving the quality of its output. A unique, distinguishing aspect of our approach is that it is multimedia and interactive. We aim to convey all the knowledge extracted with graphics, to point out the most important findings in natural language, and also to provide support for the interactive exploration of the corpus from which the knowledge was extracted.