We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations. Our work combines the strengths of multiple recent approaches while addressing their weaknesses. Moreover, we leverage recent advances in word embeddings and graph degeneracy applied to NLP to take exterior semantic knowledge into account, and to design custom diversity and informativeness measures. Experiments on the AMI and ICSI corpus show that our system improves on the state-of-the-art. Code and data are publicly available, and our system can be interactively tested.
Description : a end-to-end framework for abstractive meeting summarization. Community Detection is performed by (1) building an utterance graph with a logistic regression classifier, and (2) applying the CONGA algorithm. Then, before performing sentence compression with the MSCG, the authors also (3) build an entailment graph with a SVM classifier in order to eliminate redundant and less informative utterances. In addition, the authors propose the use of WordNet @cite_0 during the MSCG building phase to capture lexical knowledge between words and thus generate more abstractive compressions, and of a language model when re-ranking the shortest paths, to favor fluent compressions.
@cite_0
2081580037
Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet 1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4].