通过重新审视MMR准则改进更新摘要
Improving Update Summarization by Revisiting the MMR Criterion
摘要：
本文描述了一种基于双重最大化准则的多文档更新摘要方法。一个最大的边缘相关性，如标准，修改，所谓的SMMR，是用来选择句子，接近的主题，并在同一时间，远离句子中使用的已经阅读的文件。然后，通过组装高排名的材料和应用一些基于规则的语言后处理，以获得长度减少和保持一致性的摘要。通过参加文本分析会议（TAC）2008年的评估活动，我们已经表明，我们的方法取得了可喜的成果。
英文摘要：
This paper describes a method for multi-document update summarization that relies on a double maximization criterion. A Maximal Marginal Relevance like criterion, modified and so called Smmr, is used to select sentences that are close to the topic and at the same time, distant from sentences used in already read documents. Summaries are then generated by assembling the high ranked material and applying some ruled-based linguistic post-processing in order to obtain length reduction and maintain coherency. Through a participation to the Text Analysis Conference (TAC) 2008 evaluation campaign, we have shown that our method achieves promising results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
