  Word embedding methods revolve around learning continuous distributed vector
representations of words with neural networks, which can capture semantic
and/or syntactic cues, and in turn be used to induce similarity measures among
words, sentences and documents in context. Celebrated methods can be
categorized as prediction-based and count-based methods according to the
training objectives and model architectures. Their pros and cons have been
extensively analyzed and evaluated in recent studies, but there is relatively
less work continuing the line of research to develop an enhanced learning
method that brings together the advantages of the two model families. In
addition, the interpretation of the learned word representations still remains
somewhat opaque. Motivated by the observations and considering the pressing
need, this paper presents a novel method for learning the word representations,
which not only inherits the advantages of classic word embedding methods but
also offers a clearer and more rigorous interpretation of the learned word
representations. Built upon the proposed word embedding method, we further
formulate a translation-based language modeling framework for the extractive
speech summarization task. A series of empirical evaluations demonstrate the
effectiveness of the proposed word representation learning and language
modeling techniques in extractive speech summarization.
