Graph-Community Detection for Cross-Document Topic Segment Relationship Identification
Table 5: Best results obtained with graph-community detection algorithms. In bold are the best results obtained.
['[BOLD] Algorithm', '[BOLD] Weighting', '[BOLD] Scoring Function', '[ITALIC]  [BOLD] A [BOLD] R [BOLD] I', '[ITALIC]  [BOLD] F [BOLD] 1', '[ITALIC]  [BOLD] A [BOLD] c [BOLD] c']
It should be noted that some of the techniques are not sensitive to edge weight. Another aspect to consider is that there are words in the documents that should not be taken into account, since they are either too common or too rare to be representative of a subtopic in a document. In this experiment we also adopted this approach by setting for each segment a cutoff at the top-100 words with the highest tf-idf score. It is possible to observe that the Walktraps algorithm performed better in all evaluation metrics.