HipoRank: Incorporating Hierarchical and Positional Information into Graph-based Unsupervised Long Document Extractive Summarization
Table 6: Results on the PubMed validation set with different positional function or different hierarchical information.
['[BOLD] Model', '[BOLD] ROUGE-1', '[BOLD] ROUGE-2', '[BOLD] ROUGE-L']
We can see that using the wrong positional function hurts the model’s performance when comparing no positional function with lead bias function. Our boundary positional function outperforms the lead or no positional functions significantly. We observe that using a non-fully-connected graph with hierarchical information results in better performance than the flat fully-connected graph.