Label-Guided Scientific Abstract Generation with a Siamese Network Using Knowledge Graphs

Knowledge graphs convey precise semantic information that can be effectively interpreted by neural networks, and generating descriptive text based on these graphs places significant emphasis on content consistency. However, knowledge graphs are inadequate for providing additional linguistic features such as paragraph structure and expressive modes, making it challenging to ensure content coherence in generating text that spans multiple sentences. This lack of coherence can further compromise the overall consistency of the content within a paragraph. In this work, we present the generation of scientific abstracts by leveraging knowledge graphs, with a focus on enhancing both content consistency and coherence. In particular, we construct the ACL Abstract Graph Dataset (ACL-AGD) which pairs knowledge graphs with text, incorporating sentence labels to guide text structure and diverse expressions. We then implement a Siamese network to complement and concretize the entities and relations based on paragraph structure by accomplishing two tasks: graph-to-text generation and entity alignment. Extensive experiments demonstrate that the logical paragraphs generated by our method exhibit entities with a uniform position distribution and appropriate frequency. In terms of content, our method accurately represents the information encoded in the knowledge graph, prevents the generation of irrelevant content, and achieves coherent and non-redundant adjacent sentences, even with a shared knowledge graph.