Word embeddings are undoubtedly very useful components in many NLP tasks.
We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations.
Graph kernels have been successfully applied to many graph classification problems.
We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research.
In this paper, we present a novel document similarity measure based on the definition of a graph kernel between pairs of documents.
Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations.
Ranked #3 on Graph Classification on RE-M12K
Recently, there has been a lot of activity in learning distributed representations of words in vector spaces.
We introduce a novel method to extract keywords from meeting speech in real-time.