The　abundance　and　ubiquity　of　graphs　(e.g.,　online　social　networks　such　as　Google　and　Facebook;　bibliographic　graphs　such　as　DBLP)　necessitates　the　effective　and　efficient　search　over　them.　Given　a　set　of　keywords　that　can　identify　a　data　subject　(DS),　a　recently　proposed　keyword　search　paradigm　produces　a　set　of　object　summaries　(OSs)　as　results.　An　OS　is　a　tree　structure　rooted　at　the　DS　node　(i.e.,　a　node　containing　the　keywords)　with　surrounding　nodes　that　summarize　all　data　held　on　the　graph　about　the　DS.　OS　snippets,　denoted　as　size-l　OSs,　have　also　been　investigated.　A　size-l　OS　is　a　partial　OS　containing　l　nodes　such　that　the　summation　of　their　importance　scores　results　in　the　maximum　possible　total　score.　However,　the　set　of　nodes　that　maximize　the　total　importance　score　may　result　in　an　uninformative　size-l　OSs,　as　very　important　nodes　may　be　repeated　in　it,　dominating　other　representative　information.　In　view　of　this　limitation,　in　this　paper,　we　investigate　the　effective　and　efficient　generation　of　two　novel　types　of　OS　snippets,　i.e.,　diverse　and　proportional　size-l　OSs,　denoted　as　DSize-l　and　PSize-l　OSs.　Namely,　besides　the　importance　of　each　node,　we　also　consider　its　pairwise　relevance　(similarity)　to　the　other　nodes　in　the　OS　and　the　snippet.　We　conduct　an　extensive　evaluation　on　two　real　graphs　(DBLP　and　Google).　We　verify　effectiveness　by　collecting　user　feedback,　e.g.,　by　asking　DBLP　authors　(i.e.,　the　DSs　themselves)　to　evaluate　our　results.　In　addition,　we　verify　the　efficiency　of　our　algorithms　and　evaluate　the　quality　of　the　snippets　that　they　produce.