Exploring Graph Bushy Paths to Improve Statistical Multilingual Automatic Text Summarization

Abstract : Statistical extractive summarization is one of the most exploited approach in automatic text summarization due to its generation speed, implementation easiness and multilingual property. We want to improve statistical sentence scoring by exploring a simple, yet powerful, property of graphs called bushy paths represented by the number of node’s neighbors. A graph of similarities is constructed in order to select candidate sentences. Statistical features such as sentence position, sentence length, term frequency and sentences similarities are used to get a primary score for each candidate sentence. The graph is used again to enhance the primary score by using bushy paths property. Also, we tried to exploit the graph in order to enhance summary’s coherence. We experimented our method using MultiLing’15 workshop’s corpora for multilingual single document summarization. Using graph properties can improve statistical scoring without loosing the multilingualism of the method.
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Abdelkrime Aries, Djamel Zegour, Walid Hidouci. Exploring Graph Bushy Paths to Improve Statistical Multilingual Automatic Text Summarization. 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. pp.78-89, ⟨10.1007/978-3-319-89743-1_8⟩. ⟨hal-01913917⟩

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