The　goal　of　multi-document　summarization　(MDS)　is　to　generate　a　comprehensive　and　concise　summary　from　multiple　documents,　which　should　not　only　be　grammatically　correct　but　also　semantically　contains　the　refined　content　of　the　overall　texts.　Existing　summarizers　based　on　sequential　pre-trained　large　language　models　often　cognize　documents　as　linear　sequences,　which　overlook　the　hierarchical　structure　correlations　of　sentences　and　paragraphs　within　or　between　documents.　Additionally,　those　models　also　have　limitations　in　handling　long　text　input.　To　alleviate　these　two　problems,　a　multi-document　summarization　model　is　proposed,　with　a　heterogeneous　graph　of　sentences,　paragraphs　and　documents,　called　HeterMDS,　to　uncover　deep　semantic　meanings　and　local-global　context　within　documents.　By　integrating　large　language　model　and　graph　encoder　with　bootstrapped　graph　latents,　the　proposed　HeterMDS　can　learn　a　semantically　rich　document　representation　and　generate　a　coherent,　concise　and　fact-consistent　summary.　It　can　be　flexibly　applied　to　current　pre-trained　language　models,　effectively　improving　their　performance　in　MDS.　Extensive　experiment　results　can　verify　the　effectiveness　of　the　proposed　HeterMDS　and　its　contained　modules,　and　demonstrate　its　competitiveness　against　the　state-of-the-art　models.　©　2016　IEEE.