Chinese Discourse Parsing on Hierarchical Topic Graphs

Discourse parsing aims to help understand the structure and semantics of discourse
by mining the intrinsic structured information of the text. Most existing methods lack guidance from topic information in modeling discourse units, resulting in inconsistencies in semantic modeling at various levels. Therefore, we propose a Chinese discourse parsing method on hierarchical topic graphs, interacting with topic information and textual semantic information at different levels. In particular, we use GPT-4 to generate topic information at different levels. Then, we construct the topic information into a three-level hierarchical topic graph by referring to the original discourse unit division, allowing the core information at different levels to merge. The experiments on both Chinese UCDTB and English RST-DT demonstrate the effectiveness of our proposed method.