Enriched semantic graphs for extractive text summarization

Authors Antonio F. G. Sevilla
Alberto Fernández-Isabel
Alberto Díaz Esteban
Published Advances in Artificial Intelligence, Springer, 2016
Keywords Semantic Graph, Information Extraction, Text Summarization, Natural Language Processing
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, graphbased 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.
    author="Sevilla, Antonio F. G.
        and Fern{\'a}ndez-Isabel, Alberto
        and D{\'i}az, Alberto",
    title="Enriched Semantic Graphs for Extractive Text Summarization",
    booktitle="Advances in Artificial Intelligence",
    publisher="Springer International Publishing",
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