Summarization of Spontaneous Conversations

Most speech summarization research is conducted on broadcast news. In our viewpoint, spontaneous conversations are a more "typical" speech source that distinguishes speech summarization from text summarization, and hence a more appropriate domain for studying speech summarization. For example, spontaneous conversations contain more spoken-language characteristics, e.g. disfluencies and false starts. They are also more vulnerable to ASR errors. Previous research has studied some aspects of this type of data, but this paper addresses the problem further in several important respects. First, we summarize spontaneous conversations with features of a wide variety that have not been explored before. Second, we examine the role of disfluencies in summarization, which in all previous work was either not explicitly handled or removed as noise. Third, we breakdown and analyze the impact of WER on the individual features for summarization.