A machine learning approach to sentence ordering for multidocument summarization and its evaluation
A machine learning approach to sentence ordering for multidocument summarization and its evaluation
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DOI：
10.1007/11562214_55
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发表时间：
2005-01-01
期刊：
影响因子：
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通讯作者：
Ishizuka, M
中科院分区：
文献类型：
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作者：
Bollegala, D;Okazaki, N;Ishizuka, M
在自然语言生成应用中，信息排序是一项困难而又重要的任务。错误的信息顺序不仅使其难以理解，而且还会向读者传达完全不同的信息。本文提出了一种从一组人类有序文本中学习排序的算法。我们的模型由一组排序专家组成。每个专家在两个句子之间给出自己的优先级。我们把这些偏好和顺序句结合起来。我们还提出了两个评价句子排序的新指标。实验结果表明，该算法在所有评价指标上都优于现有方法。
Ordering information is a difficult but a important task for natural language generation applications. A wrong order of information not only makes it difficult to understand, but also conveys an entirely different idea to the reader. This paper proposes an algorithm that learns orderings from a set of human ordered texts. Our model consists of a set of ordering experts. Each expert gives its precedence preference between two sentences. We combine these preferences and order sentences. We also propose two new metrics for the evaluation of sentence orderings. Our experimental results show that the proposed algorithm outperforms the existing methods in all evaluation metrics.