Abstract
We propose a conceptual graph-based framework for abstractive text summarization. While syntactic or partial semantic representations of texts have been used in literature, complete semantic representations have not been explored for this purpose. We use a complete semantic representation, namely, conceptual graph structures, composed of concepts and conceptual relations. To summarize a conceptual graph, we remove the nodes that represent less important content, and apply certain operations on the resulting smaller conceptual graphs. We measure the importance of nodes on weighted conceptual graphs by the HITS algorithm, augmented with some heuristics based on VerbNet semantic patterns. Our experimental results are promising.
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Miranda-Jiménez, S., Gelbukh, A., Sidorov, G. (2013). Summarizing Conceptual Graphs for Automatic Summarization Task. In: Pfeiffer, H.D., Ignatov, D.I., Poelmans, J., Gadiraju, N. (eds) Conceptual Structures for STEM Research and Education. ICCS 2013. Lecture Notes in Computer Science(), vol 7735. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35786-2_18
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DOI: https://doi.org/10.1007/978-3-642-35786-2_18
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