Matching Article Pairs with Graphical Decomposition and Convolutions
Table 2: Accuracy and F1-score results of different algorithms on CNSE and CNSS datasets.
['Baselines', 'CNSE Acc', 'CNSE F1', 'CNSS Acc', 'CNSS F1', 'Our models', 'CNSE Acc', 'CNSE F1', 'CNSS Acc', 'CNSS F1']
In model names, “CIG” means that in CIG, we directly use keywords as concepts without community detection, whereas “CIGcd” means that each concept vertex in the CIG contains a set of keywords grouped via community detection. To generate the matching vector on each vertex, “Siam” indicates the use of Siamese encoder, while “Sim” indicates the use of term-based similarity encoder, as shown in Fig. “GCN” means that we convolve the local matching vectors on vertices through GCN layers. Finally, “BERTg” or “Simg” indicates the use of additional global features given by BERT or the five term-based similarity metrics mentioned in Sec. Our model achieves the best performance on both two datasets and significantly outperforms all other methods. This can be attributed to two reasons. First, as the input of article pairs are re-organized into Concept Interaction Graphs, the two documents are aligned along the corresponding semantic units for easier concept-wise comparison. Second, our model encodes local comparisons around different semantic units into local matching vectors, and aggregate them via graph convolutions, taking semantic topologies into consideration. Therefore, it solves the problem of matching documents via divide-and-conquer, which is suitable for handling long text. Impact of Graphical Decomposition. Comparing method XI with methods The key difference is that our method XI compares a pair of articles over a CIG in per-vertex decomposed fashion. We can see that the performance of method XI is significantly better than methods I-VI. Similarly, comparing our method XIV with methods VII-IX, they all use the same term-based similarities. However, our method achieves significantly better performance by using graphical decomposition. Therefore, we conclude that graphical decomposition can greatly improve long text matching performance.