阅读理解是目前NLP领域的一个研究热点.阅读理解中好的复杂问题解答策略不仅要进行答案句的抽取,还要对答案句进行融合、生成相应的答案,但是目前的研究大多集中在前者.该文针对复杂问题解答中的句子融合进行研究,提出了一种兼顾句子重要信息、问题关联度与句子流畅度的句子融合方法.该方法的主要思想为:首先,基于句子拆分和词重要度选择待融合部分;然后,基于词对齐进行句子相同信息的合并;最后,利用基于依存关系、二元语言模型及词重要度的整数线性规划优化生成句子.在历年高考阅读理解数据集上的测试结果表明,该方法取得了82.62％的F值,同时更好地保证了结果的可读性及信息量.%Reading comprehension system is a research focus in natural language processing.In these systems,both answer extraction and sentence fusion are necessary for answering complex problems.This paper focuses on the techniques of sentence fusion for complex problems,and presents a method considering the sentence importance,the relevancy to queries and the sentence readability.This method first chooses the partsto be fused based on sentence division and word salience.Then,the repeated contents are merged by word alignments.Finally,the sentences are generated based on the integer linear optimization,which utilizes dependency relations,the language model and word salient.The experiments on reading comprehension datasets in college entrance examinations achieve an F-measure of 82.62 ％.
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