Abstract
Semantic graph representation of text is an important part of natural language processing applications such as text summarisation. We have studied two ways of constructing the semantic graph of a document from dependency parsing of its sentences. The first graph is derived from the subject-object-verb representation of sentence, and the second graph is derived from considering more dependency relations in the sentence by a shortest distance dependency path calculation, resulting in a dense semantic graph. We have shown through experiments that dense semantic graphs gives better performance in semantic graph based unsupervised extractive text summarisation.
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Joshi, M., Wang, H., McClean, S. (2018). Dense Semantic Graph and Its Application in Single Document Summarisation. In: Lai, C., Giuliani, A., Semeraro, G. (eds) Emerging Ideas on Information Filtering and Retrieval. Studies in Computational Intelligence, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-319-68392-8_4
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