摘要:案件舆情摘要是从涉及特定案件的新闻文本簇中，抽取能够概括其主题信息的几个句子作为摘要.案件舆情摘要可以看作特定领域的多文档摘要，与一般的摘要任务相比，可以通过一些贯穿于整个文本簇的案件要素来表征其主题信息.在文本簇中，由于句子与句子之间存在关联关系，案件要素与句子亦存在着不同程度的关联关系，这些关联关系对摘要句的抽取有着重要的作用.提出了基于案件要素句子关联图卷积的案件文本摘要方法，采用图的结构来对多文本簇进行建模，句子作为主节点，词和案件要素作为辅助节点来增强句子之间的关联关系，利用多种特征计算不同节点间的关联关系.然后，使用图卷积神经网络学习句子关联图，并对句子进行分类得到候选摘要句.最后，通过去重和排序得到案件舆情摘要.在收集到的案件舆情摘要数据集上进行实验，结果表明：提出的方法相比基准模型取得了更好的效果，引入要素及句子关联图对案件多文档摘要有很好的效果.



Abstract:The case-related public opinion summarization is the task of extracting a few sentences that can summarize the subject information from some case-related news documents. The case-related public opinion summarization can be regarded as a multi-document summarization in a specific field. Compared with the general multi-document summarization, the topic information can be characterized by some case elements that run through the entire text cluster. In text clusters, sentences and sentences are associated with each other, case elements also have associations of varying degree with sentences. These associations play an important role in extracting abstract sentences. A case-related public opinion summarization method based on graph convolution of sentence association graph with case elements is proposed, which uses graph structure to model all text clusters, with sentences as the main node, words and case elements as auxiliary nodes to enhance the relationship between sentences. Multiple features are used to calculate the relationship between different nodes. Then, graph convolutional neural network is used to learn this sentence association graph, and the sentence is classified to obtain the candidate summary sentence. Finally, the sentence is deduplicated and ranked to obtain the case-related public opinion summarization. Experiments are performed on the case-related public opinion summary dataset. The results show that the method achieves better results than the benchmark model, indicating that both the composition method and the graph convolution learning method are effective.



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