In　order　to　quickly　obtain　the　main　information　contained　in　news　documents,　reduce　redundant　information　and　improve　the　efficiency　of　finding　news　with　specific　content.　A　Chinese　text　summarization　method　based　on　TF-IDF　is　proposed.　This　method　uses　TF-IDF　to　calculate　the　importance　of　each　word　in　the　article,　and　calculates　the　TF-IDF　of　each　sentence　based　on　the　TF-IDF　value　of　the　word.　In　order　to　avoid　the　effect　of　sentence　length　on　the　calculation　of　sentence　TF-IDF　value.　The　sliding　window　is　used　to　calculate　the　mean　of　all　words　TF-IDF　in　each　sliding　window　using　the　given　sliding　window　size.　Use　the　value　of　the　sliding　window　with　the　largest　mean　value　in　each　sentence　as　the　TF-IDF　value　of　the　sentence.　Combined　with　other　feature　of　the　sentence,　the　importance　of　the　sentence　is　calculated.　The　sentences　with　the　specified　length　or　number　of　words　are　intercepted　and　arranged　according　to　the　order　of　the　articles　to　form　a　summarization　of　the　article.　After　comparison　experiments,　the　method　is　superior　to　the　text　summarization　scheme　based　on　TextRank　method　in　terms　of　efficiency　and　effect.　©　2020,　Springer　Nature　Switzerland　AG.