Title: Coherent Extractive Multi-Document Summarization

Speaker: Gagan Bansal

Abstract: The popularity of Multi-Document Summarization continues to rise in the era of information overload. The task involves generating a quality summary of a collection of related documents. The domain of application is huge, from summarizing new articles to automatically generating Wikipedia articles. I present two systems that are aimed at solving this complex problem for summarizing news articles. The first is coherent summarizer for short document collections and the latter produces hierarchical summaries for large collections. Both the systems use a joint model of sentence selection and reordering which results in more coherent summaries. Manual experiments done on a crowdsourcing platform (Amazon Mechanical Turk) show that summaries generated by these systems are prefered more by the users than the previous state-of-the-art.

Relevant papers: NAACL'13 and ACL'14