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We present a graph theoretic technique for automatic text summarization aimed at producing extractive summaries of a single document. In our system, called as SumGraph, text is represented as a graph with sentences as nodes while weights on the links represent intra-sentence dissimilarity. Novelty of our approach lies in the use of Pathfinder Network Scaling (PFnet) technique representing conceptual organization of the text which in turn is used to compute importance of a sentence in the text. Importance of a sentence is defined using its centrality in the PFnet. Use of Latent Semantic Analysis (LSA) is also investigated. PFnet and LSA have been shown to model human aspects of semantic memory and linguistic acquisition respectively. The system is empirically evaluated on DUC2001 and DUC2002 datasets using ROUGE measure. Results show that SumGraph performs better than other systems, including a commercial summarizer. Use of LSA did not show any improvement in ROUGE score. We also show that SumGraph is statistically different than other methods using a non-parametric statistical test.