In　the　era　of　big　data,　the　number　of　internet　users　is　increasing　yearly,　and　each　user　receives　a　massive　amount　of　information　every　day.　The　low-value　density　of　massive　text　data　makes　it　difficult　to　utilize　its　information.　In　the　face　of　this　problem,　text　summarization　generation　technology　that　can　extract　the　paper＇s　leading　content　and　condense　the　paper＇s　main　idea　is　a　powerful　solution.　In　text　summarization　generation,　abstractive　summarization　techniques　with　high　readability,　clear　expression,　and　closer　proximity　to　human　language　habits　are　often　favored.　However,　mainstream　technologies　still　have　issues　like　summarizations＇　detachment　from　focus.　In　this　paper,　keyword　information　is　employed　to　join　the　text　summarization　generation　task,　and　a　keyword　information　module　is　added　to　the　Transformer　to　better　perform　the　summarization　generation　task.　Finally,　the　ROUGE　evaluation　standard　is　used　to　compare　the　performance　of　the　above　models　on　the　large-scale　Chinese　short　text　summarization　(LCSTS)　dataset.　The　experimental　results　show　that　the　model　generates　higher-quality　summarizations　using　keyword　information,　and　the　related　methods　have　considerable　potential.　Future　work　can　explore　better　model　structures,　more　suitable　methods　for　utilizing　keyword　information,　and　more　accurate　keyword　extraction　techniques　to　improve　the　quality　of　abstractive　summarizations.　©　2023　IEEE.