The　Relational　ranking　method　applies　authority-based　ranking　in　relational　dataset　that　can　be　modeled　as　graphs　considering　also　their　tuples＇　values.　Authority　directions　from　tuples　that　contain　the　given　keywords　and　transfer　to　their　corresponding　neighboring　nodes　in　accordance　with　their　values　and　semantic　connections.　From　our　previous　work,　ObjectRank　extends　to　ValueRank　that　also　takes　into　account　the　value　of　tuples　in　authority　transfer　flows.　In　a　maked　difference　from　ObjectRank,　which　only　considers　authority　flows　through　relationships,　it　is　only　valid　in　the　bibliographic　databases　e.g.　DBLP　dataset,　ValueRank　facilitates　the　estimation　of　importance　for　any　databases,　e.g.　trading　databases,　etc.　A　relational　keyword　search　paradigm　Object　Summary　(denote　as　OS)　is　proposed　recently,　given　a　set　of　keywords,　a　group　of　Object　Summaries　as　its　query　result.　An　OS　is　a　multilevel-tree　data　structure,　in　which　node　(namely　the　tuple　with　keywords)　is　OS＇s　root　node,　and　the　surrounding　nodes　are　the　summary　of　all　data　on　the　graph.　But,　some　of　these　trees　have　a　very　large　in　total　number　of　tuples,　size-l　OSs　are　the　OS　snippets,　have　also　been　investigated　using　ValueRank.We　evaluated　the　real　bibliographical　dataset　and　Microsoft　business　databases　to　verify　of　our　proposed　approach.