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Graph-Based Abstractive Summarization: Compression of Semantic Graphs
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Author(s): Balaji Jagan (Anna University, India), Ranjani Parthasarathi (Anna University, India)and Geetha T. V. (Anna University, India)
Copyright: 2018
Pages: 26
Source title:
Innovations, Developments, and Applications of Semantic Web and Information Systems
Source Author(s)/Editor(s): Miltiadis D. Lytras (American College of Greece, Greece), Naif Aljohani (King Abdulaziz University, Saudi Arabia), Ernesto Damiani (University of Milan, Italy)and Kwok Tai Chui (The Open University of Hong Kong, Hong Kong)
DOI: 10.4018/978-1-5225-5042-6.ch009
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Abstract
Customization of information from web documents is an immense job that involves mainly the shortening of original texts. Extractive methods use surface level and statistical features for the selection of important sentences. In contrast, abstractive methods need a formal semantic representation, where the selection of important components and the rephrasing of the selected components are carried out using the semantic features associated with the words as well as the context. In this paper, we propose a semi-supervised bootstrapping approach for the identification of important components for abstractive summarization. The input to the proposed approach is a fully connected semantic graph of a document, where the semantic graphs are constructed for sentences, which are then connected by synonym concepts and co-referring entities to form a complete semantic graph. The direction of the traversal of nodes is determined by a modified spreading activation algorithm, where the importance of the nodes and edges are decided, based on the node and its connected edges under consideration.
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