Domain-Independent Abstract Generation for Focused Meeting Summarization

author: Lu Wang, Department of Computer Science, Cornell University
published: Oct. 2, 2013,   recorded: August 2013,   views: 2331


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We address the challenge of generating natural language abstractive summaries for spoken meetings in a domain-independent fashion. We apply Multiple-Sequence Alignment to induce abstract generation templates that can be used for different domains. An Overgenerate-and-Rank strategy is utilized to produce and rank candidate abstracts. Experiments using in-domain and out-of-domain training on disparate corpora show that our system uniformly outperforms state-of-the-art supervised extract-based approaches. In addition, human judges rate our system summaries significantly higher than compared systems in fluency and overall quality.

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