Identifying important nodes with strong propagation capabilities in road
networks is a significant topic in the field of urban planning. However,
existing methods for evaluating nodes importance consider only topological
information and traffic volumes, ignoring the diversity of characteristics in
road networks, such as the number of lanes and average speed of road segments,
limiting their performance. To address this issue, this paper proposes a graph
learning-based node ranking method (MGL2Rank) that integrates the rich
characteristics of the road network. In this method, we first develop a
sampling algorithm (MGWalk) that utilizes multi-graph fusion to establish
association between road segments based on their attributes. Then, an embedding
module is proposed to learn latent representation for each road segment.
Finally, the obtained node representation is used to learn importance ranking
of road segments. We conduct simulation experiments on the regional road
network of Shenyang city and demonstrate the effectiveness of our proposed
method. The data and source code of MGL2Rank are available at
this https URL

本论文提出了一种基于图学习的节点排序方法 (MGL2Rank)，将道路网络的多样性特征集成在其中，包括道路路段的车道数和平均速度，实现了路网节点的重要性排序。我们采用多图融合的采样算法 (MGWalk) 来建立道路路段之间的关联，利用嵌入式模块学习每个路段的潜在表征，最终获得节点表征来学习道路路段的重要性。在实验中，我们以沈阳市的区域道路网络为例，证明了该方法的有效性。

基于多图融合的道路网络节点重要性排序学习

Learning to Rank the Importance of Nodes in Road Networks Based on  Multi-Graph Fusion

This paper focuses on the end-to-end abstractive summarization of a single
product review without supervision. We assume that a review can be described as
a discourse tree, in which the summary is the root, and the child sentences
explain their parent in detail. By recursively estimating a parent from its
children, our model learns the latent discourse tree without an external parser
and generates a concise summary. We also introduce an architecture that ranks
the importance of each sentence on the tree to support summary generation
focusing on the main review point. The experimental results demonstrate that
our model is competitive with or outperforms other unsupervised approaches. In
particular, for relatively long reviews, it achieves a competitive or better
performance than supervised models. The induced tree shows that the child
sentences provide additional information about their parent, and the generated
summary abstracts the entire review.

通过递归估计父句子与子句子之间的关系，组成一个无须外部解析器的潜在篇章树，以此进行无监督的单篇产品评论的纯摘要，并介绍了一种句子重要性排序的架构来生成集中在主要评论点的摘要。研究结果表明，该模型在相对长的评论中表现竞争或超越受监督模型的表现，其生成的摘要抽象了整个评论。

通过学习潜在的话语结构及其排名实现评论的非监督式神经单篇摘要

Unsupervised Neural Single-Document Summarization of Reviews via  Learning Latent Discourse Structure and its Ranking

Extracting valuable facts or informative summaries from multi-dimensional
tables, i.e. insight mining, is an important task in data analysis and business
intelligence. However, ranking the importance of insights remains a challenging
and unexplored task. The main challenge is that explicitly scoring an insight
or giving it a rank requires a thorough understanding of the tables and costs a
lot of manual efforts, which leads to the lack of available training data for
the insight ranking problem. In this paper, we propose an insight ranking model
that consists of two parts: A neural ranking model explores the data
characteristics, such as the header semantics and the data statistical
features, and a memory network model introduces table structure and context
information into the ranking process. We also build a dataset with text
assistance. Experimental results show that our approach largely improves the
ranking precision as reported in multi evaluation metrics.

本研究提出了一个建立在神经和记忆网络上的见解重要性排序模型，同时构建了一个文本辅助数据集。实验结果表明，我们的方法在多个评估指标中大大提高了排序精度。