Long story short-Global unsupervised models for keyphrase, based meeting summarization

Korbinian Riedhammer (1) , Benoit Favre (2) , Dilek Hakkani-Tur (1)

1 ICSI - International Computer Science Institute [Berkeley]
2 LIF - Laboratoire d'informatique Fondamentale de Marseille

We analyze and compare two different methods for unsupervised extractive spontaneous speech summarization in the meeting domain. Based on utterance comparison, we introduce an optimal formulation for the widely used greedy maximum marginal relevance (MMR) algorithm. Following the idea that information is spread over the utterances in form of concepts, we describe a system which finds an optimal selection of utterances covering as many unique important concepts as possible. Both optimization problems are formulated as an integer linear program (ILP) and solved using public domain software. We analyze and discuss the performance of both approaches using various evaluation setups on two well studied meeting corpora. We conclude on the benefits and drawbacks of the presented models and give an outlook on future aspects to improve extractive meeting summarization

Domains: Computer Science [cs]; Computation and Language [cs.CL]

Korbinian Riedhammer, Benoit Favre, Dilek Hakkani-Tur. Long story short-Global unsupervised models for keyphrase, based meeting summarization. Speech Communication, 2010, 52 (10), pp.801-815. ⟨10.1016/j.specom.2010.06.002⟩. ⟨hal-01194272⟩