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Visualization-enabled multi-document summarization by Iterative Residual Rescaling

机译：通过迭代残差重缩放实现可视化的多文档摘要

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摘要

This paper describes a novel approach to multi-document summarization, which explicitly addresses the problem of detecting, and retaining for the summary, multiple themes in document collections. We place equal emphasis on the processes of theme identification and theme presentation. For the former, we apply Iterative Residual Rescaling (IRR); for the latter, we argue for graphical display elements. IRR is an algorithm designed to account for correlations between words and to construct multi-dimensional topical space indicative of relationships among linguistic objects (documents, phrases, and sentences). Summaries are composed of objects with certain properties, derived by exploiting the many-to-many relationships in such a space. Given their inherent complexity, our multi-faceted summaries benefit from a visualization environment. We discuss some essential features of such an environment.
机译：本文介绍了一种新颖的多文档摘要方法，该方法明确解决了在文档集中检测和保留多个主题的问题。我们同样重视主题识别和主题展示的过程。对于前者，我们应用迭代残差重定标度（IRR）；对于后者，我们主张使用图形显示元素。 IRR是一种算法，旨在解决单词之间的相关性并构建表示语言对象（文档，短语和句子）之间关系的多维主题空间。摘要由具有某些属性的对象组成，这些对象是通过利用该空间中的多对多关系而派生的。鉴于其固有的复杂性，我们的多方面摘要可从可视化环境中受益。我们讨论了这种环境的一些基本特征。

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