Abstractive Meeting Summarization with Entailment and Fusion

We propose a novel end-to-end framework for abstractive meeting summarization. We cluster sentences in the input into communities and build an entailment graph over the sentence communities to identify and select the most relevant sentences. We then aggregate those selected sentences by means of a word graph model. We exploit a ranking strategy to select the best path in the word graph as an abstract sentence. Despite not relying on the syntactic structure, our approach significantly outperforms previous models for meeting summarization in terms of informativeness. Moreover, the longer sentences generated by our method are competitive with shorter sentences generated by the previous word graph model in terms of grammaticality.