ILP-based Compressive Speech Summarization with Content Word Coverage Maximization and Its Oracle Performance Analysis

摘要

We propose an integer linear programming (ILP)-based compressive speech summarization method that maximizes the coverage of content words in a resultant summary. It is an unsupervised method and, under the designed constraints, it performs a single-step globally optimal summarization of a given long speech recording, which is decoded as a confusion network form of an automatic speech recognition (ASR) hypothesis sequence. It selects as many different content words as possible from the speech input that inevitably includes a high level of redundancy (e.g. the repetition of the same word) under a given length constraint. In experiments using a lecture speech corpus, we obtained higher summarization performance in terms of ROUGE scores than with a baseline extractive summarization method. We further conduct experimental analyses to obtain the oracle (upper bound) performance of the summarization methods. The analysis results show that the oracle performance is very high even though the ASR hypotheses include recognition errors. It is significantly higher than the system performance and, in addition, the oracle performance of the compressive method is significantly higher than that of the extractive method. These results confirm that our method is a promising approach.