Generating interactive multimedia summaries of evaluative text.

许多组织和个人每天都面临着管理大量文本数据的挑战。一个重要的应用是评价性文本，即任何表达对实体的评价为积极或消极的文件。评估性信息对决策具有战略意义。例如，许多网站收集大量消费者对消费电子产品的在线评论。尽管这些文献对产品设计师、规划人员和制造商具有重大的战略价值，但有效处理这些信息仍然是一项复杂而耗时的任务。另一个例子是，在出国旅行之前，许多用户在预订之前会浏览旅行日志和在线用户对酒店的评论。然而，有这么多的评价网站，用户被淹没在评论。在这些和许多其他类似的情况下，自动化的解决方案有可能极大地减少成本和时间，以跟上不断增长的评估文献的数量。我们不要求用户一次通读一篇评论，而是建议以多粒度的方式忠实地总结文本评论，这样用户就可以根据自己的需求和时间限制选择合适的粒度级别。虽然在如何从文本中提取评价信息方面已经进行了大量的工作，但很少注意如何有效地总结提取的信息并将其呈现给用户。在之前的工作中，我们已经开始探索这个问题，在这个项目中，我们建议将我们的工作扩展到不同的方向。总的来说，我们的目标是消除简化的假设，增加摘要过程输入的复杂性，并提高其输出的质量。我们的方法的一个独特的、显著的方面是它是多媒体和互动的。我们的目标是用图形传达所有提取的知识，指出自然语言中最重要的发现，并为提取知识的语料库的交互式探索提供支持。

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.