We present an exploration of generative probabilistic models for multi-document summarization . Beginning with a simple word frequency based model ( Nenkova and Vanderwende , 2005 ) , we construct a sequence of models each injecting more structure into the representation of document set content and exhibiting ROUGE gains along the way . Our final model , HIERSUM , utilizes a hierarchical LDA-style model ( Blei et al . , 2004 ) to represent content specificity as a hierarchy of topic vocabulary distributions . At the task of producing generic DUC-style summaries , HIERSUM yields state-of-the-art ROUGE performance and in pairwise user evaluation strongly outperforms Toutanova et al . ( 2007 ) 's state-of-the-art discriminative system . We also explore HIERSUM 's capacity to produce multiple ' topical summaries ' in order to facilitate content discovery and navigation . Over the past several years , there has been much interest in the task of multi-document summarization . In the common Document Understanding Conference ( DUC ) formulation of the task , a system takes as input a document set as well as a short description of desired summary focus and outputs a word length limited summary . 1 To avoid the problem of generating cogent sentences , many systems opt for an extractive approach , selecting sentences from the document set which best reflect its core content . 2 There are several approaches to modeling document content : simple word frequency-based methods ( Luhn , 1958 ; Nenkova and Vanderwende , 2005 ) , graph-based approaches ( Radev , 2004 ; Wan and Yang , 2006 ) , as well as more linguistically motivated techniques ( Mckeown et al . , 1999 ; Leskovec et al . , 2005 ; Harabagiu et al . , 2007 ) . Another strand of work ( Barzilay and Lee , 2004 ; Daumé III and Marcu , 2006 ; Eisenstein and Barzilay , 2008 ) , has explored the use of structured probabilistic topic models to represent document content . However , little has been done to directly compare the benefit of complex content models to simpler surface ones for generic multi-document summarization . In this work we examine a series of content models for multi-document summarization and argue that LDA-style probabilistic topic models ( Blei et al . , 2003 ) can offer state-of-the-art summarization quality as measured by automatic metrics ( see section 5.1 ) and manual user evaluation ( see section 5.2 ) . We also contend that they provide convenient building blocks for adding more structure to a summarization model . In particular , we utilize a variation of the hierarchical LDA topic model ( Blei et al . , 2004 ) to discover multiple specific ' subtopics ' within a document set . The resulting model , HIERSUM ( see section 3.4 ) , can produce general summaries as well as summaries for any of the learned sub-topics . In this paper we have presented an exploration of content models for multi-document summarization and demonstrated that the use of structured topic models can benefit summarization quality as measured by automatic and manual metrics .

Challenge: There have been only a few works that investigate how complex content models perform on multi-document summarization.
Approach: They benchmark a series of existing generative probabilistic models and also propose an LDA-style which can model multiple "sub-topics".
Outcome: LDA-style models can perform well on both automatic and manual evaluations and the proposed model can produce generic and also sub-topic-focused summaries.