Factual　consistency　of　text　summarization　is　consistent　with　information　of　the　source　document　and　the　summarization.　Recent　researches　have　shown　that　there　are　large　number　of　factual　inconsistencies　existed　in　the　outputs　of　abstractive　summarization　models.　It　is　important　to　design　a　method　that　can　detect　and　evaluate　the　error　of　fact　inconsistency.　Most　of　existing　methods　based　on　natural　language　inference　have　insufficient　ability　to　extract　key　content　of　source　document　and　infer　the　information　of　content.　This　paper　improves　the　accuracy　of　consistency　assessment　model　for　summarization　by　multi-attention　mechanism.　Firstly,　the　key　sentences　are　selected　according　to　sentence-BERT,　which　is　fine-turning　on　pre-trained　language　model.　Each　evidence　and　claim　are　formed　into　separate　sentence　pairs　and　sent　to　the　BERT　encoder　to　obtain　the　vectors　of　the　sentence　pairs　representation,　then　it　achieves　evidence　reasoning　by　the　vectors　combined　with　ESIM.　Finally,　the　graph　attention　network　is　used　to　complete　the　aggregation　of　inferential　information　to　obtain　the　fact　consistency　assessment　result.　Experimental　results　show　that　this　algorithm　is　compared　with　several　typical　algorithms　in　common　datasets　in　the　field,　and　its　feasibility　and　effectiveness　are　verified.　©　2023　Journal　of　Computer　Engineering　and　Applications　Beijing　Co.,　Ltd.;　Science　Press.　All　rights　reserved.