{"title":"基于句法序列的局部主题信息提取","authors":"Paul Villavicencio, Toyohide Watanabe","doi":"10.1504/IJKWI.2012.050853","DOIUrl":null,"url":null,"abstract":"In order to support the processes of reading digital research articles, we propose a summarisation method focused on using the localised topic information of documents. The objective is to produce summaries containing topics which are detailed in a document rather than those representing a general overview. We obtain the summaries by ranking sentences based on key-terms cooccurring throughout the document structure, and key-terms within the syntactic structure of sentences. Our idea of using the concept of syntactic structures is that authors usually have their own writing styles, represented by grammatical structures. In this paper we present a description of the method along with an evaluation, comparing our method to other existing summarisation methods.","PeriodicalId":113936,"journal":{"name":"Int. J. Knowl. Web Intell.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Localised topic information extraction for summarisation using syntactic sequences\",\"authors\":\"Paul Villavicencio, Toyohide Watanabe\",\"doi\":\"10.1504/IJKWI.2012.050853\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to support the processes of reading digital research articles, we propose a summarisation method focused on using the localised topic information of documents. The objective is to produce summaries containing topics which are detailed in a document rather than those representing a general overview. We obtain the summaries by ranking sentences based on key-terms cooccurring throughout the document structure, and key-terms within the syntactic structure of sentences. Our idea of using the concept of syntactic structures is that authors usually have their own writing styles, represented by grammatical structures. In this paper we present a description of the method along with an evaluation, comparing our method to other existing summarisation methods.\",\"PeriodicalId\":113936,\"journal\":{\"name\":\"Int. J. Knowl. Web Intell.\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Knowl. Web Intell.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/IJKWI.2012.050853\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Knowl. Web Intell.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJKWI.2012.050853","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Localised topic information extraction for summarisation using syntactic sequences
In order to support the processes of reading digital research articles, we propose a summarisation method focused on using the localised topic information of documents. The objective is to produce summaries containing topics which are detailed in a document rather than those representing a general overview. We obtain the summaries by ranking sentences based on key-terms cooccurring throughout the document structure, and key-terms within the syntactic structure of sentences. Our idea of using the concept of syntactic structures is that authors usually have their own writing styles, represented by grammatical structures. In this paper we present a description of the method along with an evaluation, comparing our method to other existing summarisation methods.