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The crucial aspect of extractive document summarization lies in understanding the interrelations between sentences. Documents inherently comprise a multitude of sentences, and sentence-level models frequently fail to consider the relationships between distantly-placed sentences, resulting in the omission of significant information in the summary. Moreover, information within documents tends to be distributed sparsely, challenging the efficacy of sentence-level models. In the realm of heterogeneous graph neural networks, it has been observed that semantic nodes with varying levels of granularity encapsulate distinct semantic connections. Initially, the incorporation of edge features into the computation of dynamic graph attention networks is performed to account for node relationships. Subsequently, given the multiplicity of topics in a document or a set of documents, a topic model is employed to extract topic-specific features and the probability distribution linking these topics with sentence nodes. Last but not least, the model defines nodes with different levels of granularity—ranging from documents and topics to sentences—and these various nodes necessitate different propagation widths and depths for capturing intricate relationships in the information being disseminated. Adaptive measures are taken to learn the importance and correlation between nodes of different granularities in terms of both width and depth. Experimental evidence from two benchmark datasets highlights the superior performance of the proposed model, as assessed by ROUGE metrics, in comparison to existing approaches, even in the absence of pre-trained language models. Additionally, an ablation study confirms the positive impact of each individual module on the model's ROUGE scores.

The rapid expansion of urbanization both in scale and population leads to a series of serious urban diseases, which become a huge obstacle to the healthy and sustainable development of cities. To alleviate these problems and challenges, China launched a smart city construction program in the past decade and has taken the lead in smart city construction in the world. However, there is still a lack of reflection and summary on the practice of smart cities in China. Based on the definition and concept of smart city, this paper points out the internal and external driving factors of China’s smart city development, then summarizes the four major characteristics of China’s smart city construction practice, and explores the main problems existing in the process of China’s smart city construction. Through the reflection and summary, we can facilitate development of smart cities in China, provide useful reference to urban planners and smart city practitioners in other countries and regions, and promote the healthy and sustainable development of cities.