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We present an exploration of generative probabilistic models for multi-document summarization. Beginning with a simple word… Expand The Markov Random Walk model has been recently exploited for multi-document summarization by making use of the link relationships… Expand Multi-document summarization aims to create a compressed summary while retaining the main characteristics of the original set of… Expand In this work we study the theoretical and empirical properties of various global inference algorithms for multi-document… Expand Topic-focused multi-document summarization aims to produce a summary biased to a given topic or user profile. This paper presents… Expand In many decision‐making scenarios, people can benefit from knowing what other people's opinions are. As more and more evaluative… Expand We present a multi-document summarizer, MEAD, which generates summaries using cluster centroids produced by a topic detection and… Expand We present a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic… Expand This paper discusses a text extraction approach to multi-document summarization that builds on single-document summarization… Expand We present a method to automatically generate a concise summary by identifying and synthesizing similar elements across related… Expand