2014 | OriginalPaper | Chapter
Random Walks for Opinion Summarization on Conversations
Authors : Zhongqing Wang, Liyuan Lin, Shoushan Li, Guodong Zhou
Published in: Natural Language Processing and Chinese Computing
Publisher: Springer Berlin Heidelberg
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Opinion summarization on conversations aims to generate a sentimental summary for a dialogue and is shown to be much more challenging than traditional topic-based summarization and general opinion summarization, due to its specific characteristics. In this study, we propose a graph-based framework to opinion summarization on conversations. In particular, a random walk model is proposed to globally rank the utterances in a conversation. The main advantage of our approach is its ability of integrating various kinds of important information, such as utterance length, opinion, and dialogue structure, into a graph to better represent the utterances in a conversation and the relationship among them. Besides, a global ranking algorithm is proposed to optimize the graph. Empirical evaluation on the Switchboard corpus demonstrates the effectiveness of our approach.