基于案件要素指导的涉案舆情新闻文本摘要方法

Case-involved Public Opinion News Summarization with Case Elements Guidance

涉案舆情新闻文本摘要任务是从涉及特定案件的舆情新闻文本中,获取重要信息作为其简短摘要,因此对于相关人员快速掌控舆情态势具有重要作用。涉案舆情新闻文本摘要相比开放域文本摘要任务,通常涉及特定的案件要素,这些要素对摘要生成过程有重要的指导作用。因此,该文结合深度学习框架,提出了一种融入案件要素的涉案舆情新闻文本摘要方法。首先构建涉案舆情新闻摘要数据集并定义相关案件要素,然后通过注意力机制将案件要素信息融入新闻文本的词、句子双层编码过程中,生成带有案件要素信息的新闻文本表征,最后利用多特征分类层对句子进行分类。为了验证算法有效性,在构造的涉案舆情新闻摘要数据集上进行实验。实验结果表明,该方法相比基准模型取得了更好的效果,具有有效性和先进性。

The summary task of the public opinion news on a judical case is to obtain important information on public comments on the case in a short summary.Compared with the task of text summarization in open domain,this kind of summary usually involves specific case elements that are of great guiding effect in the process of summary generation.Therefore,a case-related news text summarization method is proposed based on deep learning framework.First,a dataset of the public opinion news summary is collected,and the case elements are defined.Then,through the attention mechanism,the case element information is integrated into the double-layer coding process of words and sentences in the news text to generate the news text representation that contains the case element information.Finally,the multi-feature classification layer is used to classify the sentences.Experiments are conducted on the public opinion news summary datasetand show that the proposed method has better performance than the base model.