1 Anexpressofacertain withoutanyexnationsandcomment.It'sunnecessarytoknowwhowritesthesummary.(ANSI)Aconciseandaccurateexpressofthewithoutanyex nationandcomment.Asummaryisindependentontheauthorofthesummary.(ISO214-1976(E))2 性/指示性 以性的形式表述文獻中信息價值較高的部分，而以指示性的形式表述其余部分的。 3SummaryClassifiedbyuser'sGenericSummarizationUser-querySummarizationClassifiedbytext ClassifiedbySummarizationBasedonExtractionSummarizationBasedonUnderstandingClassifiedbyneedSupervisedSummarizationUnsupervisedSummarization4 5ABriefHistoryofDUC,holdbyNIST,fromByMarkT.MayburyandInderjeetMani6 Vivisimo公司 78內部評價方法(IntrinsicMethods)：在提供參考的前提下，以參考為基準評價系統的質量。通常情況下，系統摘要與參考越吻合，其質量越高。外部評價方法(ExtrinsicMethods)：不需要提供參考，利用文檔代替原文檔夠提高應用性能的被認為是質量好的摘9Evaluation-- 得與目標 (coselectionrate)。 Evaluation-- “。”“：”“；”“！”“？”；為使專家與機械具有可比性，只專家和機械的句子都按照在原文 n

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Content:weight=Sumoftopicterms’weights;Length:weight/=Position:weight*=positionConten;Stigmawords;Stigma:Conten;Stigmawords;Redundancy:weight*=&Repair e.g:Edmundson的 詞(Bonus [Luhn,1958]：能夠指示文章 實詞的個數來計算句子的權值。[V.A.Oswald]主張句子的權值應按其所含 ANES(AutormaticNewsExtractionSystem)作所謂的目次性，這也很受歡統計表明：大部分科技文獻的標題都能基本反映葉句，刪句留主干句的方 (如氣象預報等)。 基于理解的自動常包含語法分析、語義分析、信息提取和生成，作者應屬于此。分層的，理解比低層理解更為手工人員在編制時并不一定通結束語及其論題句，以發現其，再挑選句子并修飾稍加組織生成。文獻，不同用戶點和觀察角度可能不同，的結果應當不同。SentenceSentenceRepresenteachsentenceasafeatureComputescorebasedonPresentinorderinwhichtheyoccurinPostprocessingtomakesummarymoreEliminateredundantDeletesubordinateclauses,SentenceImportance-- [G. Sentence 冗余性消除 ? ? A Sigir95paperonsummarization A TrainablesentenceProposedalgorithmisappliedtoitsowndescription(thepaper)FeatureFixed-phraseCertainphrasesindicatesummary,e.g.“inParagraphParagraphinitial/finalmorelikelytobeThematicwordRepetitionisanindicatorofUppercasewordUppercaseoftenindicatesnamedentities.Sentencelengthcut-Summarysentenceshouldbe>5Hand-labelsentencesintrainingset(good/badsummarysentences)Trainclassifiertodistinguishgood/badsummarysentencesModelused:Na?veCanranksentencesaccordingtoscoreandshowtopntouser. EvaluationofBaseline(choosefirstnsentences):Overallperformance(42-44%)notveryHowever,thereismorethanoneClusteringbasedClustering Sentences 及MMRQuery-SpecificAgenericsummarymakesnoassumptionaboutthereader’sinterests.Query-specificsummariesarespecializedforasingleinformationneed,thequery.Summarizationismucheasierifwehaveadescriptionofwhattheuserwants.Recallfromlast -typeexcerpts–simplyshowincontextMMR le ????? -??1- i 術和IGR(InformationGainRatio)技術結合起來，稱為MMI-MS MMRArgmax[Sim1(Di,Q)-(1-)maxSim2(Di,DjDiR\ DMMI-MSArgmax[Imp(Si)-(1-)maxSimj(Si,SjSiSS\ IGR_sum(w,D) CCset(D

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