{"title":"自动文本摘要中句子分数加厚的方法","authors":"Michael George","doi":"10.5121/IJDKP.2017.7607","DOIUrl":null,"url":null,"abstract":"In our study we will use approach that combine Natural language processing NLP with Term occurrences to improve the quality of important sentences selection by thickening sentence score along with reducing the number of long sentences that would be included in the final summarization. There are sixteen known methods for automatic text summarization. In our paper we utilized Term frequency approach and built an algorithm to re filter sentences score.","PeriodicalId":131153,"journal":{"name":"International Journal of Data Mining & Knowledge Management Process","volume":"113 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Approach for Thickening Sentence Score for Automatic Text Summarization\",\"authors\":\"Michael George\",\"doi\":\"10.5121/IJDKP.2017.7607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In our study we will use approach that combine Natural language processing NLP with Term occurrences to improve the quality of important sentences selection by thickening sentence score along with reducing the number of long sentences that would be included in the final summarization. There are sixteen known methods for automatic text summarization. In our paper we utilized Term frequency approach and built an algorithm to re filter sentences score.\",\"PeriodicalId\":131153,\"journal\":{\"name\":\"International Journal of Data Mining & Knowledge Management Process\",\"volume\":\"113 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Data Mining & Knowledge Management Process\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/IJDKP.2017.7607\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Data Mining & Knowledge Management Process","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/IJDKP.2017.7607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Approach for Thickening Sentence Score for Automatic Text Summarization
In our study we will use approach that combine Natural language processing NLP with Term occurrences to improve the quality of important sentences selection by thickening sentence score along with reducing the number of long sentences that would be included in the final summarization. There are sixteen known methods for automatic text summarization. In our paper we utilized Term frequency approach and built an algorithm to re filter sentences score.