A Bottom-Up Approach to Sentence Ordering for Multi-Document Summarization
A Bottom-Up Approach to Sentence Ordering for Multi-Document Summarization
复制标题
DOI：
10.3115/1220175.1220224
复制
发表时间：
2006-07
期刊：
影响因子：
--
通讯作者：
Danushka Bollegala;Naoaki Okazaki;M. Ishizuka
中科院分区：
文献类型：
--
作者：
Danushka Bollegala;Naoaki Okazaki;M. Ishizuka
对于生成自然语言文本（例如多文档摘要、问答和概念到文本生成）的应用程序来说，排序信息是一项困难但重要的任务。在多文档摘要中，信息是从一组源文档中选择的。然而，摘要中信息的不正确排序可能会使读者感到困惑并降低摘要的可读性。因此，在多文档摘要中正确排序信息至关重要。我们提出了一种自下而上的方法来排列提取的句子以进行多文档摘要。为了捕获两个文本片段（例如句子）的关联和顺序，我们定义了四个标准：时间顺序、主题紧密度、优先顺序和连续性。这些标准通过监督学习方法整合到一个标准中。我们根据标准反复将两个文本片段连接成一个片段，直到获得所有句子排列好的整体片段。我们使用主观评分以及自动评估措施来评估所提出的方法和大量基线产生的句子顺序。我们引入了平均连续性，这是一种摘要中句子排序的自动评估方法，并研究了它对于该任务的适用性。
Ordering information is a difficult but important task for applications generating natural language texts such as multi-document summarization, question answering, and concept-to-text generation. In multi-document summarization, information is selected from a set of source documents. However, improper ordering of information in a summary can confuse the reader and deteriorate the readability of the summary. Therefore, it is vital to properly order the information in multi-document summarization. We present a bottom-up approach to arrange sentences extracted for multi-document summarization. To capture the association and order of two textual segments (e.g. sentences), we define four criteria: chronology, topical-closeness, precedence, and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion, until we obtain the overall segment with all sentences arranged. We evaluate the sentence orderings produced by the proposed method and numerous baselines using subjective gradings as well as automatic evaluation measures. We introduce the average continuity, an automatic evaluation measure of sentence ordering in a summary, and investigate its appropriateness for this task.