Conceptual Graphs as Framework for Summarizing Short Texts

In this paper, a conceptual graph-based framework for summarizing short texts is proposed. A semantic representation is implemented through conceptual graph structures that consist of concepts and conceptual relations that stand for texts. To summarize conceptual graphs, the most important nodes are selected using a set of operations: generalization, association, ranking, and pruning, which are described. The importance of nodes on weighted conceptual graphs is measured using a modified version of HITS algorithm. In addition, some heuristic rules are used to keep coherent structures based on information from WordNet hierarchy of concepts and VerbNet semantic patterns of verbs. The experimental results show that this approach is effective in summarizing short texts.