Muhammad Irfan Liaqat, Isma Hamid, Qamar Nawaz, Nida Shafique
{"title":"Abstractive Text Summarization Using Hybrid Technique of Summarization","authors":"Muhammad Irfan Liaqat, Isma Hamid, Qamar Nawaz, Nida Shafique","doi":"10.1109/iccsn55126.2022.9817599","DOIUrl":null,"url":null,"abstract":"Abstractive text summarization focuses at compress the document into a shorter form while keeping the connotation intact. The extractive summary can select chunks of sentences that are very related to the document, on the other hand, an abstractive summary can generate a summary based on extracted keywords. This research proposed an abstractive text summarization model, it gets data from source data (e.g., Daily Mail/CNN) or other documents and two summaries of this are generated. one from a philologist and the other by proposed model. The summary generated by the philologist kept as a model to compare with the machine-generated summary. The proposed model increased the accuracy and the readability of the summary.","PeriodicalId":108888,"journal":{"name":"2022 14th International Conference on Communication Software and Networks (ICCSN)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 14th International Conference on Communication Software and Networks (ICCSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iccsn55126.2022.9817599","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Abstractive text summarization focuses at compress the document into a shorter form while keeping the connotation intact. The extractive summary can select chunks of sentences that are very related to the document, on the other hand, an abstractive summary can generate a summary based on extracted keywords. This research proposed an abstractive text summarization model, it gets data from source data (e.g., Daily Mail/CNN) or other documents and two summaries of this are generated. one from a philologist and the other by proposed model. The summary generated by the philologist kept as a model to compare with the machine-generated summary. The proposed model increased the accuracy and the readability of the summary.
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