LexPageRank: Prestige in Multi-Document Text Summarization

LexPageRank: Prestige in Multi-Document Text Summarization G¨unes¸ Erkan � , Dragomir R. Radev �� � � Department of EECS, � School of Information University of Michigan � gerkan,radev � @umich.edu Abstract Multidocument extractive summarization relies on the concept of sentence centrality to identify the most important sentences in a document. Central- ity is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We are now consid- ering an approach for computing sentence impor- tance based on the concept of eigenvector centrality (prestige) that we call LexPageRank. In this model, a sentence connectivity matrix is constructed based on cosine similarity. If the cosine similarity be- tween two sentences exceeds a particular predefined threshold, a corresponding edge is added to the con- nectivity matrix. We provide an evaluation of our method on DUC 2004 data. The results show that our approach outperforms centroid-based summa- rization and is quite successful compared to other summarization systems. 1 Introduction Text summarization is the process of automatically creating a compressed version of a given text that provides useful information for the user. In this pa- per, we focus on multi-document generic text sum- marization, where the goal is to produce a summary of multiple documents about the same, but unspeci- fied topic. Our summarization approach is to assess the cen- trality of each sentence in a cluster and include the most important ones in the summary. In Section 2, we present centroid-based summarization, a well- known method for judging sentence centrality. Then we introduce two new measures for centrality, De- gree and LexPageRank, inspired from the “prestige” concept in social networks and based on our new ap- proach. We compare our new methods and centroid- based summarization using a feature-based generic summarization toolkit, MEAD, and show that new features outperform Centroid in most of the cases. Test data for our experiments is taken from Docu- ment Understanding Conferences (DUC) 2004 sum- marization evaluation to compare our system also with other state-of-the-art summarization systems. 2 Sentence centrality and centroid-based summarization Extractive summarization produces summaries by choosing a subset of the sentences in the original documents. This process can be viewed as choosing the most central sentences in a (multi-document) cluster that give the necessary and enough amount of information related to the main theme of the clus- ter. Centrality of a sentence is often defined in terms of the centrality of the words that it contains. A common way of assessing word centrality is to look at the centroid. The centroid of a cluster is a pseudo- document which consists of words that have fre- quency*IDF scores above a predefined threshold. In centroid-based summarization (Radev et al., 2000), the sentences that contain more words from the cen- troid of the cluster are considered as central. For- mally, the centroid score of a sentence is the co- sine of the angle between the centroid vector of the whole cluster and the individual centroid of the sen- tence. …

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