Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
This work addresses the problem of generating concise summaries from meeting transcripts without annotations, which is incremental as it builds on and improves existing methods.
The authors tackled unsupervised abstractive meeting summarization by introducing a graph-based framework that combines multi-sentence compression and budgeted submodular maximization, achieving state-of-the-art results on the AMI and ICSI corpora.
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.