Abstract
Automatic extraction of semantic information from unstructured text has always been an important goal of natural language processing. While the best structure for semantic information is still undecided, graph-based representations enjoy a healthy following. Some of these representations are extracted directly from the text and external knowledge, while others are built from linguistic insight, created from the deep analysis of the surface text. In this document a combination of both approaches is outlined, and its application for extractive text summarization is described. A pipeline for this task has been implemented, and its results evaluated against a collection of documents from the DUC2003 competition. Graph construction is fully automatic, and summary creation is based on the clustering of conceptual nodes. Different configurations for the semantic graphs are used and compared, and their fitness for the task discussed.
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Acknowledgements
This work is funded by ConCreTe. The project ConCreTe ackn-owledges the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under FET grant number 611733.
This research is funded by the Spanish Ministry of Economy and Competitive-ness and the European Regional Development Fund (TIN2015-66655-R (MINECO/FEDER)).
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Sevilla, A.F.G., Fernández-Isabel, A., Díaz, A. (2016). Enriched Semantic Graphs for Extractive Text Summarization. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_20
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DOI: https://doi.org/10.1007/978-3-319-44636-3_20
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