Graph-Community Detection for Cross-Document Topic Segment Relationship Identification
Table 6: Summary of the results in the document relationship identification task.
['[BOLD] Algorithm', '[ITALIC]  [BOLD] A [BOLD] R [BOLD] I', '[ITALIC]  [BOLD] F [BOLD] 1', '[ITALIC]  [BOLD] A [BOLD] c [BOLD] c']
The Walktraps approach provided the best performance in the document relationship identification task, obtaining better results in all evaluation metrics used. The most important conclusion from these results is that graph-community detection-based techniques are more suitable for the relationship identification task. The difference in the evaluation metrics is substantial, with a value twice as high in ARI, and a 10% increase in F1 and Acc. The problem with clustering techniques is that they rely on a similarity space, which is not appropriate for the nature of this task. Given the results obtained, we claim that the document relationship identification task should be done by exploring network properties of a co-occurrence graph using a graph-community detection approach.