Kuo J.-J.Chen H.-H.2019-07-102019-07-10200815300226https://scholars.lib.ntu.edu.tw/handle/123456789/413184Summary generation for multiple documents poses a number of issues including sentence selection, sentence ordering, and sentence reduction over single-document summarization. In addition, the temporal resolution among extracted sentences is also important. This article considers informative words and event words to deal with multidocument summarization. These words indicate the important concepts and relationships in a document or among a set of documents, and can be used to select salient sentences. We present a temporal resolution algorithm, using focusing time and coreference chains, to convert Chinese temporal expressions in a document into calendrical forms. Moreover, we consider the last calendrical form of a sentence as a sentence time stamp to address sentence ordering. Informative words, event words, and temporal words are introduced to a sentence reduction algorithm, which deals with both length constraints and information coverage. Experiments on Chinese-news data sets show significant improvements of both information coverage and readability. ? 2008 ACM.Latent semantic analysisMultidocument summary generationSentence orderingSentence reductionSentence selectionTemporal processing[SDGs]SDG4journal article10.1145/1330291.13302942-s2.0-39149124104https://www.scopus.com/inward/record.uri?eid=2-s2.0-39149124104&doi=10.1145%2f1330291.1330294&partnerID=40&md5=043158a0297424dd58945a13c2e80756