挖掘技术textmining以提供文献内容梗概为目的不加评论和补充

1 Anexpressofacertain withoutanyexnationsandcomment.It'sunnecessarytoknowwhowritesthesummary.(ANSI)Aconciseandaccurateexpressofthewithoutanyex nationandcomment.Asummaryisindependentontheauthorofthesummary.(ISO214-1976(E))2 性/指示性 以性的形式表述文献中信息价值较高的部分，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内部评价方法(IntrinsicMethods)：在提供参考的前提下，以参考为基准评价系统的质量。通常情况下，系统摘要与参考越吻合，其质量越高。外部评价方法(ExtrinsicMethods)：不需要提供参考，利用文档代替原文档够提高应用性能的被认为是质量好的摘9Evaluation-- 得与目标 (coselectionrate)。 Evaluation-- “。”“：”“；”“！”“？”；为使专家与机械具有可比性，只专家和机械的句子都按照在原文 n

RecallNhm/ PrecisionNhm ➢ UnderstandingConference(DUC)isaseriesofsummarizationevaluationsthathavebeenconductedbytheNationalInstituteofStandardsandTechnology(NIST)since2001.Itsgoalistofurtherprogressinautomatictextsummarizationandenableresearcherstoparticipateinlarge-scaleexperimentsinboththedevelopmentandevaluationofsummarizationSince2008, DUChasmovedtotheTextysisConference(TAC)QuestionAnswering;RecognizingSummarizationKeyword ymostEasytoHardtoread,poorrepresentationofSentenceExtractkeySummariesoftendon’treadNaturallanguageunderstanding/HardtodoSomethingbetweenthelasttwo SummarizationWebThisis

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Content:weight=Sumoftopicterms’weights;Length:weight/=Position:weight*=positionConten;Stigmawords;Stigma:Conten;Stigmawords;Redundancy:weight*=&Repair e.g:Edmundson的 词(Bonus [Luhn,1958]：能够指示文章 实词的个数来计算句子的权值。[V.A.Oswald]主张句子的权值应按其所含 ANES(AutormaticNewsExtractionSystem)作所谓的目次性，这也很受欢统计表明：大部分科技文献的标题都能基本反映叶句，删句留主干句的方 (如气象预报等)。 基于理解的自动常包含语法分析、语义分析、信息提取和生成，作者应属于此。分层的，理解比低层理解更为手工人员在编制时并不一定通结束语及其论题句，以发现其，再挑选句子并修饰稍加组织生成。文献，不同用户点和观察角度可能不同，的结果应当不同。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:NaïveCanranksentencesaccordingtoscoreandshowtopntouser. EvaluationofBaseline(choosefirstnsentences):Overallperformance(42-44%)notveryHowever,thereismorethanoneClusteringbasedClustering Sentences 及MMRQuery-SpecificAgenericsummarymakesnoassumptionaboutthereader’sinterests.Query-specificsummariesarespecializedforasingleinformationneed,thequery.Summarizationismucheasierifwehaveadescriptionofwhattheuserwants.Recallfromlast -typeexcerpts–simplyshowincontextMMR le ⬧🞏∙⬝ -🕿1- i 术和IGR(InformationGainRatio)技术结合起来，称为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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