{"title":"关于你最喜欢的数据的个人故事","authors":"C. Labbé, C. Roncancio, D. Bras","doi":"10.18653/v1/W15-4727","DOIUrl":null,"url":null,"abstract":"A controlled use of omnipresent data can leverage a potential of services never reached before. In this paper, we propose a user driven approach to take advantage of massive data streams. Our solution, named Stream2Text, relies on a personalized and continuous refinement of data to generate texts (in natural language) that provide a tailored synthesis of relevant data. It enables monitoring by a wide range of users as text streams can be shared on social networks or used individually on mobile devices.","PeriodicalId":307841,"journal":{"name":"European Workshop on Natural Language Generation","volume":"245 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A Personal Storytelling about Your Favorite Data\",\"authors\":\"C. Labbé, C. Roncancio, D. Bras\",\"doi\":\"10.18653/v1/W15-4727\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A controlled use of omnipresent data can leverage a potential of services never reached before. In this paper, we propose a user driven approach to take advantage of massive data streams. Our solution, named Stream2Text, relies on a personalized and continuous refinement of data to generate texts (in natural language) that provide a tailored synthesis of relevant data. It enables monitoring by a wide range of users as text streams can be shared on social networks or used individually on mobile devices.\",\"PeriodicalId\":307841,\"journal\":{\"name\":\"European Workshop on Natural Language Generation\",\"volume\":\"245 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Workshop on Natural Language Generation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/W15-4727\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Workshop on Natural Language Generation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/W15-4727","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A controlled use of omnipresent data can leverage a potential of services never reached before. In this paper, we propose a user driven approach to take advantage of massive data streams. Our solution, named Stream2Text, relies on a personalized and continuous refinement of data to generate texts (in natural language) that provide a tailored synthesis of relevant data. It enables monitoring by a wide range of users as text streams can be shared on social networks or used individually on mobile devices.
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