N. Karanikolas, E. Galiotou, Christodoulos Tsoulloftas
{"title":"A Workbench for Extractive Summarizing Methods","authors":"N. Karanikolas, E. Galiotou, Christodoulos Tsoulloftas","doi":"10.1109/PCi.2012.67","DOIUrl":null,"url":null,"abstract":"We present a software workbench for testing available/well known methods for text summarization. The software was designed and implemented in order to incorporate alternative proposed methodologies of extractive summarization. Therefore, the provided methods do not aim at understanding and condensing the meanings of an amount of original sentences into a smaller number of sentences not occurring in the original text. It simply extracts a subset of the original sentences which are the most (promising as being) relevant for expressing the meaning of the text. Our purpose is twofold: a) to provide a utility for benchmarking of well known summarization methods to researchers or/and professionals who are interested in implementing summarization products, b) to perform our own evaluations of the well known extractive summarization methods.","PeriodicalId":131195,"journal":{"name":"2012 16th Panhellenic Conference on Informatics","volume":"141 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 16th Panhellenic Conference on Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PCi.2012.67","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
We present a software workbench for testing available/well known methods for text summarization. The software was designed and implemented in order to incorporate alternative proposed methodologies of extractive summarization. Therefore, the provided methods do not aim at understanding and condensing the meanings of an amount of original sentences into a smaller number of sentences not occurring in the original text. It simply extracts a subset of the original sentences which are the most (promising as being) relevant for expressing the meaning of the text. Our purpose is twofold: a) to provide a utility for benchmarking of well known summarization methods to researchers or/and professionals who are interested in implementing summarization products, b) to perform our own evaluations of the well known extractive summarization methods.