Abstract
With vast amounts of text being available in electronic format, such as news and social media, automatic multi-document summarization can help extract the most important information. We present and evaluate a novel method for automatic extractive multi-document summarization. The method is purely combinatorial, based on bicliques in the bipartite word-sentence occurrence graph. It is particularly suited for collections of very short, independently written texts (often single sentences) with many repeated phrases, such as customer reviews of products. The method can run in subquadratic time in the number of documents, which is relevant for the application to large collections of documents.
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Acknowledgments
This work has been supported by Grant IIS11-0089 from the Swedish Foundation for Strategic Research (SSF), for the project “Data-driven secure business intelligence”. We thank our former master’s students Emma Bogren and Johan Toft for drawing our attention to similarity joins, and the members of our Algorithms group and collaborators at the companies Recorded Future and Findwise for many discussions.
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Muhammad, A.S., Damaschke, P., Mogren, O. (2016). Summarizing Online User Reviews Using Bicliques. In: Freivalds, R., Engels, G., Catania, B. (eds) SOFSEM 2016: Theory and Practice of Computer Science. SOFSEM 2016. Lecture Notes in Computer Science(), vol 9587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49192-8_46
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DOI: https://doi.org/10.1007/978-3-662-49192-8_46
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