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Báo cáo khoa học: "Learning From Collective Human Behavior to Introduce Diversity in Lexical Choice"

We analyze collective discourse, a collective human behavior in content generation, and show that it exhibits diversity, a property of general collective systems. Using extensive analysis, we propose a novel paradigm for designing summary generation systems that reflect the diversity of perspectives seen in reallife collective summarization. We analyze 50 sets of summaries written by human about the same story or artifact and investigate the diversity of perspectives across these summaries. | Learning From Collective Human Behavior to Introduce Diversity in Lexical Choice Vahed Qazvinian Department of EECS University of Michigan Ann Arbor MI vahed@umich.edu Dragomir R. Radev School of Information Department of EECS University of Michigan Ann Arbor MI radev@umich.edu Abstract We analyze collective discourse a collective human behavior in content generation and show that it exhibits diversity a property of general collective systems. Using extensive analysis we propose a novel paradigm for designing summary generation systems that reflect the diversity of perspectives seen in real-life collective summarization. We analyze 50 sets of summaries written by human about the same story or artifact and investigate the diversity of perspectives across these summaries. We show how different summaries use various phrasal information units i.e. nuggets to express the same atomic semantic units called factoids. Finally we present a ranker that employs distributional similarities to build a network of words and captures the diversity of perspectives by detecting communities in this network. Our experiments show how our system outperforms a wide range of other document ranking systems that leverage diversity. 1 Introduction In sociology the term collective behavior is used to denote mass activities that are not centrally coordinated Blumer 1951 . Collective behavior is different from group behavior in the following ways a it involves limited social interaction b membership is fluid and c it generates weak and unconventional norms Smelser 1963 . In this paper we focus on the computational analysis of collective discourse a collective behavior seen in interactive content contribution and text summarization in online social media. In collective discourse each in 1098 dividual s behavior is largely independent of that of other individuals. In social media discourse Grosz and Sidner 1986 is often a collective reaction to an event. One scenario leading to collective reaction