实验室关于摘要抽取研究被期刊Neural Computing and Application录用
新闻来源:IR实验室       发布时间:2019/11/24 0:00:00

  近日,收到期刊《Neural Computing and Application》编辑部邮件,实验室刁宇峰等的研究工作“CRHASum: Extractive Text Summarization with Contextualized-Representation Hierarchical-Attention Summarization Network”已被录用。

  摘要:文档自动摘要是一种可应用于实际需求的正在迅速增长的应用任务。摘要抽取式文本摘要是一种通用的句子回归框架,利用外部相关信息从文档中提取句子。然而,现有的句子回归方法不能充分挖掘上下文信息和句子之间的语义关系。为了解决这一问题,我们提出了一种神经网络模型,即上下文表示层次注意摘要模型(CRHASum),该模型利用上下文信息和句子之间的关系来提高句子的回归性能用以抽取文本摘要。这个框架充分利用了它们的优点。一个优点是充分使用上下文表示的语境表征信息,另一个优点是层次注意机制能够通过使用Bi-GRU从词和句子两个层次捕捉语境关系。通过这种设计,CRHASum模型能够关注特定句子周围的重要上下文,从而实现文本摘要的抽取。我们在三个基准数据集上进行了广泛的实验。我们的模型取得与最先进方法相当的性能。同时,我们的方法在多ROUNG度量方面明显优于目前的基线方法,并且包含一些基本的有用特性。

  AbstractThe requirements for automatic document summarization that can be applied for practical applications are increasing rapidly. As a general sentence regression architecture, extractive text summarization captures sentences from a document by leveraging externally related information. However, existing sentence regression approaches have not employed features that mine the contextual information and relations among sentences. To alleviate this problem, we present a neural network model, namely, the Contextualized-Representation Hierarchical-Attention Summarization (CRHASum), that uses the contextual information and relations among sentences to improve the sentence regression performance for extractive text summarization. This framework makes the most of their advantages. One advantage is that the contextual representation is allowed to vary across linguistic context information, and the other advantage is that the hierarchical attention mechanism is able to capture the contextual relations from the word-level and sentence-level by using the Bi-GRU. With this design, the CRHASum model is capable of paying attention to the important context in the surrounding context of a given sentence for extractive text summarization. We carry out extensive experiments on three benchmark datasets. CRHASum alone can achieve comparable performance to the state-of-the-art approach. Meanwhile, our method significantly outperforms the state-of-the-art baselines in terms of multiple ROUNG metrics and includes a few basic useful features.