Real-Time Keyword Extraction from Conversations

We introduce a novel, fully unsupervised method to extract keywords from meeting speech in real-time. Our approach represents text as a word co-occurrence network and leverages the k-core graph decomposition algorithm and properties of submodular functions. We outperform multiple baselines in a real-time scenario emulated from the AMI and ICSI meeting corpora. Evaluation is conducted against both extractive and abstractive gold standard using two standard performance metrics and a newer one based on word embeddings.