Random Walks for Opinion Summarization on Conversations

Abstract

Abstract. 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. Keywords: Opinion Summarization on Conversations, Graph, Random Walk, Global Ranking. Introduction Opinion summarization aims to generate a sentimental summary on opinions in a text and has been drawing more and more attention recently in NLP due to its significant contribution to various real applications As pilots in opinion summarization on conversations, Wang and Li