  Tremendous amounts of multimedia associated with speech information are
driving an urgent need to develop efficient and effective automatic
summarization methods. To this end, we have seen rapid progress in applying
supervised deep neural network-based methods to extractive speech
summarization. More recently, the Bidirectional Encoder Representations from
Transformers (BERT) model was proposed and has achieved record-breaking success
on many natural language processing (NLP) tasks such as question answering and
language understanding. In view of this, we in this paper contextualize and
enhance the state-of-the-art BERT-based model for speech summarization, while
its contributions are at least three-fold. First, we explore the incorporation
of confidence scores into sentence representations to see if such an attempt
could help alleviate the negative effects caused by imperfect automatic speech
recognition (ASR). Secondly, we also augment the sentence embeddings obtained
from BERT with extra structural and linguistic features, such as sentence
position and inverse document frequency (IDF) statistics. Finally, we validate
the effectiveness of our proposed method on a benchmark dataset, in comparison
to several classic and celebrated speech summarization methods.
