{"title":"NMF集合?不是为了文本摘要!","authors":"Alka Khurana, Vasudha Bhatnagar","doi":"10.18653/v1/2020.insights-1.14","DOIUrl":null,"url":null,"abstract":"Non-negative Matrix Factorization (NMF) has been used for text analytics with promising results. Instability of results arising due to stochastic variations during initialization makes a case for use of ensemble technology. However, our extensive empirical investigation indicates otherwise. In this paper, we establish that ensemble summary for single document using NMF is no better than the best base model summary.","PeriodicalId":441528,"journal":{"name":"First Workshop on Insights from Negative Results in NLP","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"NMF Ensembles? Not for Text Summarization!\",\"authors\":\"Alka Khurana, Vasudha Bhatnagar\",\"doi\":\"10.18653/v1/2020.insights-1.14\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Non-negative Matrix Factorization (NMF) has been used for text analytics with promising results. Instability of results arising due to stochastic variations during initialization makes a case for use of ensemble technology. However, our extensive empirical investigation indicates otherwise. In this paper, we establish that ensemble summary for single document using NMF is no better than the best base model summary.\",\"PeriodicalId\":441528,\"journal\":{\"name\":\"First Workshop on Insights from Negative Results in NLP\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First Workshop on Insights from Negative Results in NLP\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/2020.insights-1.14\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First Workshop on Insights from Negative Results in NLP","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2020.insights-1.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Non-negative Matrix Factorization (NMF) has been used for text analytics with promising results. Instability of results arising due to stochastic variations during initialization makes a case for use of ensemble technology. However, our extensive empirical investigation indicates otherwise. In this paper, we establish that ensemble summary for single document using NMF is no better than the best base model summary.
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