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It has been reported that clustering-based topic models, which clusterhigh-quality sentence embeddings with an appropriate word selection method, cangenerate better topics than generative probabilistic topic models. However,these approaches suffer from the inability to select appropriate parameters andincomplete models that overlook the quantitative relation between words withtopics and topics with text. To solve these issues, we propose graph to topic(G2T), a simple but effective framework for topic modelling. The framework iscomposed of four modules. First, document representation is acquired usingpretrained language models. Second, a semantic graph is constructed accordingto the similarity between document representations. Third, communities indocument semantic graphs are identified, and the relationship between topicsand documents is quantified accordingly. Fourth, the word--topic distributionis computed based on a variant of TFIDF. Automatic evaluation suggests that G2Tachieved state-of-the-art performance on both English and Chinese documentswith different lengths.

Graph2topic: an opensource topic modeling framework based on sentence embedding and community detection