Prosody-Based Unsupervised Speech Summarization with Two-Layer Mutually Reinforced Random Walk

<p>This paper presents a graph-based model that integrates prosodic features into an unsupervised speech summarization framework without any lexical information. In particular it builds on previous work using mutually reinforced random walks, in which a two-layer graph structure is used to select the most salient utterances of a conversation. The model consists of one layer of utterance nodes and another layer of prosody nodes. The random walk algorithm propagates scores between layers to use shared information for selecting utterance nodes with highest scores as summaries. A comparative evaluation of our prosody-based model against several baselines on a corpus of academic multi-party meetings reveals that it performs competitively on very short summaries, and better on longer summaries according to ROUGE scores as well as the average relevance of selected utterances.</p

Retrofitting Word Vectors to Semantic Lexicons

Vector space word representations are learned from distributional information of words in large corpora. Although such statistics are semantically informative, they disregard the valuable information that is contained in semantic lexicons such as WordNet, FrameNet, and the Paraphrase Database. This paper proposes a method for refining vector space representations using relational information from semantic lexicons by encouraging linked words to have similar vector representations, and it makes no assumptions about how the input vectors were constructed. Evaluated on a battery of standard lexical semantic evaluation tasks in several languages, we obtain substantial improvements starting with a variety of word vector models. Our refinement method outperforms prior techniques for incorporating semantic lexicons into word vector training algorithms.</p