A keyphrase based approach to interactive meeting summarization

Rooted in multi-document summarization, maximum marginal rel-evance (MMR) is a widely used algorithm for meeting summariza-tion (MS). A major problem in extractive MS using MMR is nding a proper query: the centroid based query which is commonly used in the absence of a manually specied query, can not signicantly outperform a simple baseline system. We introduce a simple yet robust algorithm to automatically extract keyphrases (KP) from a meeting which can then be used as a query in the MMR algorithm. We show that the KP based system signicantly outperforms both baseline and centroid based systems. As human rened KPs show even better summarization performance, we outline how to integrate the KP approach into a graphical user interface allowing interactive summarization to match the user’s needs in terms of summary length and topic focus