To identify important utterances from open-domain spontaneous conversations, previous work has focused on using textual features that are extracted from transcripts, e.g., word frequencies and noun senses. In this paper, we summarize spontaneous conversations with features of a wide variety that have not been explored before. Experiments show that the use of speech-related features improves summarization performance. In addition, the effectiveness of individual features is examined and compared.
['Xiaodan Zhu', 'Gerald Penn']
Utterance-Level Extractive Summarization of Open-Domain Spontaneous Conversations with Rich Features