  In the scenario of unsupervised extractive summarization, learning
high-quality sentence representations is essential to select salient sentences
from the input document. Previous studies focus more on employing statistical
approaches or pre-trained language models (PLMs) to extract sentence
embeddings, while ignoring the rich information inherent in the heterogeneous
types of interaction between words and sentences. In this paper, we are the
first to propose an unsupervised extractive summarizaiton method with
heterogeneous graph embeddings (HGEs) for Chinese document. A heterogeneous
text graph is constructed to capture different granularities of interactions by
incorporating graph structural information. Moreover, our proposed graph is
general and flexible where additional nodes such as keywords can be easily
integrated. Experimental results demonstrate that our method consistently
outperforms the strong baseline in three summarization datasets.
