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      Concept mining; text mining; multi-document summarization; machine learning; restricted Boltzmann machines; MultiLing 2015 dataset. 1. Introduction Today, with the advancement of technology, there is an explosion of data available on web. Earlier the content had to be generated by the publishing houses. However, now with easy access to interne

    • Abstract


      In this paper we propose a methodology to mine concepts from documents and use these concepts to generate an objective summary of all relevant documents. We use the conceptual graph (CG) formalism as proposed by Sowa to represent the concepts and their relationships in the documents. In the present work wehave modified and extended the definition of the concept given by Sowa. The modified and extended definition is discussed in detail in section 2 of this paper. A CG of a set of relevant documents can be considered as a semantic network. The semantic network is generated by automatically extracting CG for each document and merging them into one. We discuss (i) generation of semantic network using CGs and (ii) generation of multidocument summary. Here we use restricted Boltzmann machines, a deep learning technique, for automatically extracting CGs. We have tested our methodology using MultiLing 2015 corpus. We have obtained encouraging results, which are comparable to those from the state of the art systems.

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