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Single-document and multi-document summarization techniques for email threads using sentence compression

机译:使用句子压缩的电子邮件线程的单文档和多文档摘要技术

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摘要

We present two approaches to email thread summarization: collective message summarization (CMS) applies a multi-document summarization approach, while individual message summarization (IMS) treats the problem as a sequence of single-document summarization tasks. Both approaches are implemented in our general framework driven by sentence compression. Instead of a purely extractive approach, we employ linguistic and statistical methods to generate multiple compressions, and then select from those candidates to produce a final summary. We demonstrate these ideas on the Enron email collection - a very challenging corpus because of the highly technical language. Experimental results point to two findings: that CMS represents a better approach to email thread summarization, and that current sentence compression techniques do not improve summarization performance in this genre.
机译:我们提供了两种电子邮件线程摘要方法:集体邮件摘要(CMS)应用了多文档摘要方法,而单个邮件摘要(IMS)将问题视为一系列单文档摘要任务。这两种方法都是在句子压缩驱动的通用框架中实现的。代替纯粹的提取方法,我们采用语言和统计方法来生成多个压缩,然后从这些候选中进行选择以产生最终摘要。我们在“安然”电子邮件收藏中展示了这些想法-由于具有高度的技术性语言,这是一个非常具有挑战性的语料库。实验结果表明了两个发现:CMS代表了一种更好的电子邮件线程摘要方法,而当前的句子压缩技术在这种类型中并未提高摘要性能。

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