Abstract We analyze and compare two different methods for unsupervised extractive spontaneous speech summarization in the meeting 12 domain. Based on utterance comparison, we introduce an optimal formulation for the widely used greedy maximum marginal relevance 13 (MMR) algorithm. Following the idea that information is spread over the utterances in form of concepts, we describe a system which 14 finds an optimal selection of utterances covering as many unique important concepts as possible. Both optimization problems are for-15 mulated as an integer linear program (ILP) and solved using public domain software. We analyze and discuss the performance of both 16 approaches using various evaluation setups on two well studied meeting corpora. We conclude on the benefits and drawbacks of the 17 presented models and give an outlook on future aspects to improve extractive meeting summarization.
The work REF introduces an optimal formulation for the widely used greedy maximum marginal relevance (MMR) algorithm.
Long story short -- Global unsupervised models for keyphrase based meeting summarization
SPEECH COMMUNICATION
Computer Science