A Hybrid Hierarchical Model for Multi-Document Summarization

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 815–824, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics A Hybrid Hierarchical Model for Multi-Document Summarization Asli Celikyilmaz Computer Science Department University of California, Berkeley asli@eecs.berkeley.edu Dilek Hakkani-Tur International Computer Science Institute Berkeley, CA dilek@icsi.berkeley.edu Abstract Scoring sentences in documents given ab- stract summaries created by humans is im- portant in extractive multi-document sum- marization. In this paper, we formulate ex- tractive summarization as a two step learn- ing problem building a generative model for pattern discovery and a regression model for inference. We calculate scores for sentences in document clusters based on their latent characteristics using a hi- erarchical topic model. Then, using these scores, we train a regression model based on the lexical and structural characteris- tics of the sentences, and use the model to score sentences of new documents to form a summary. Our system advances current state-of-the-art improving ROUGE scores by ∼7%. Generated summaries are less redundant and more coherent based upon manual quality evaluations. 1 Introduction Extractive approach to multi-document summa- rization (MDS) produces a summary by select- ing sentences from original documents. Doc- ument Understanding Conferences (DUC), now TAC, fosters the effort on building MDS systems, which take document clusters (documents on a same topic) and description of the desired sum- mary focus as input and output a word length lim- ited summary. Human summaries are provided for training summarization models and measuring the performance of machine generated summaries. Extractive summarization methods can be clas- sified into two groups: supervised methods that rely on provided document-summary pairs, and unsupervised methods based upon properties de- rived from document clusters. Supervised meth- ods treat the summarization task as a classifica- tion/regression problem, e.g., (Shen et al., 2007; Yeh et al., 2005). Each candidate sentence is classified as summary or non-summary based on the features that they pose and those with high- est scores are selected. Unsupervised methods aim to score sentences based on semantic group- ings extracted from documents, e.g., (Daum´eIII and Marcu, 2006; Titov and McDonald, 2008; Tang et al., 2009; Haghighi and Vanderwende, 2009; Radev et al., 2004; Branavan et al., 2009), etc. Such models can yield comparable or bet- ter performance on DUC and other evaluations, since representing documents as topic distribu- tions rather than bags of words diminishes the ef- fect of lexical variability. To the best of our knowl- edge, there is no previous research which utilizes the best features of both approaches for MDS as presented in this paper. In this paper, we present a novel approach that formulates MDS as a prediction problem based on a two-step hybrid model: a generative model for hierarchical topic discovery and a regression model for inference. We investigate if a hierarchi- cal model can be adopted to discover salient char- acteristics of sentences organized into hierarchies utilizing human generated summary text. We present a probabilistic topic model on sen- tence level building on hierarchical Latent Dirich- let Allocation …

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