Coherence is a distinctive feature in well-written documents. One method to study coherence is to analyze how sentences are ordered in a document. In Multi-document Summarization, sentences from different sources need to be ordered. Cluster-based ordering algorithms aim to study various themes or topics that are present in a set of sentences. After the clusters of sentences have been identified, sentences are ordered within each cluster in isolation. One challenge that remains is to order these clusters or paragraphs to obtain a coherent ordering of information. Inspired by the success of deep neural networks in several NLP tasks, we propose an RNN-based encoder-decoder system to predict order for a given set of loose clusters or paragraphs. Universal Sentence Encoder (USE) is used to encode paragraphs into high dimensional embeddings, which are then fed into an LSTM encoder and consecutively passed to a pointer network, which finally outputs the paragraph order. Since Wikipedia is a source of well- structured articles, it is used to generate multiple datasets. Based on our experimental results, the proposed model satisfactorily outperforms the baseline model across multiple datasets. We observe a two-fold increase in Kendall's tau values for the final paragraph orderings.

中文翻译：

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通过使用指针网络对段落进行排序来建立连贯性建模。

连贯性是写得好的文档中的一个显着特征。研究连贯性的一种方法是分析句子在文档中的排序方式。在“多文档摘要”中，需要对来自不同来源的句子进行排序。基于聚类的排序算法旨在研究一组句子中存在的各种主题或主题。在识别出句子簇之后，句子在每个簇内被单独排序。剩下的一个挑战是对这些簇或段落进行排序以获得一致的信息排序。受到深层神经网络在多个NLP任务中成功的启发，我们提出了一种基于RNN的编码器-解码器系统，以预测给定的一组松散簇或段落的顺序。通用语句编码器（USE）用于将段落编码为高维嵌入，然后将它们馈送到LSTM编码器中，并连续传递到指针网络，该指针网络最终输出段落顺序。由于Wikipedia是结构良好的文章的来源，因此可用于生成多个数据集。根据我们的实验结果，提出的模型在多个数据集上均令人满意地优于基线模型。对于最后一段排序，我们观察到Kendall的tau值增加了两倍。