It has been reported that clustering-based topic models, which cluster high-quality sentence embeddings with an appropriate word selection method, can generate better topics than generative probabilistic topic models. However, these approaches suffer from the inability to select appropriate parameters and incomplete models that overlook the quantitative relation between words with topics and topics with text. To solve these issues, we propose graph to topic (G2T), a simple but effective framework for topic modelling. The framework is composed of four modules. First, document representation is acquired using pretrained language models. Second, a semantic graph is constructed according to the similarity between document representations. Third, communities in document semantic graphs are identified, and the relationship between topics and documents is quantified accordingly. Fourth, the word--topic distribution is computed based on a variant of TFIDF. Automatic evaluation suggests that G2T achieved state-of-the-art performance on both English and Chinese documents with different lengths.

翻译：已有研究表明，基于聚类的主题模型通过结合高质量句子嵌入与恰当的词汇选择方法，能够生成优于生成式概率主题模型的主题。然而，这些方法存在参数选择困难以及模型不完整的问题，忽略了词汇与主题、主题与文本之间的定量关系。为解决这些问题，我们提出Graph2Topic (G2T)——一种简洁而有效的主题建模框架。该框架由四个模块组成：首先，利用预训练语言模型获取文档表示；其次，根据文档表示间的相似性构建语义图；然后，识别文档语义图中的社区，并据此量化主题与文档的关系；最后，基于TF-IDF的变体计算词-主题分布。自动评估结果表明，G2T在不同长度的英文与中文文档上均取得了最先进的性能。