Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown

This paper discusses a text extraction approach to multidocument summarization that builds on single-document summarization methods by using additional, available in-i formation about the document set as a whole and the relationships between the documents. Multi-document summarization differs from

We present a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also des.cdbe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single

from large-scale evaluations of multi-document summarization, empirically confirming this hypothesis. We further show that features measuring the cohesiveness of the input are highly correlated with eventual summary quality and that it is

increases. This paper provides the complete survey, which gives a comparative study about the existing multi-Document summarization techniques. This study gives an overall view about the current research issues, recent methods for summarization, data set and metrics suitable for summarization. This frame

summarization framework based on sentence-level semantic analysis and symmetric non-negative matrix factorization. We first calculate sentence-sentence similarities using semantic analysis and construct the similarity matrix. Then symmetric matrix factorization, which has been shown to be equivalent

the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints. Publication info: Proceedings of the ACL, 2004. 1

robust knowledge sources: the WordNet thesaurus, a part-of-speech tagger and shallow parser for the ldentification of nominal groups, and a segmentation algorithm derived from (Hearst, 1994) Summarization proceeds in three steps: the original text m first segmented, lexical chains are constructed, strong

conducted using the TAC-08 dataset, a well-known benchmark dataset for assessing query-based multi-document sentiment summarizers, show that QMSS (i) is highly effective in generating summaries that satisfy user’s information needs, and (ii) ranks among state-ofthe-art query-based multi-document sentiment

systems. However, the clearest advantage is demonstrated in constructing non-redundant multi-document summaries, where MMR results are clearly superior to non-MMR passage selection. 2 Maximal Marginal Relevance Most modem IR search engines produce a ranked list of retrieved documents ordered by declining