Improving hmm-based extractive summarization for multi-domain contact center dialogues

This paper reports the improvements we made to our previously proposed hidden Markov model (HMM) based summarization method for multi-domain contact center dialogues. Since the method relied on Viterbi decoding for selecting utterances to include in a summary, it had the inability to control compression rates. We enhance our method by using the forward-backward algorithm together with integer linear programming (ILP) to enable the control of compression rates, realizing summaries that contain as many domain-related utterances and as many important words as possible within a predefined character length. Using call transcripts as input, we verify the effectiveness of our enhancement.