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Generic text summarization using relevance measure and latent semantic analysis

Published: 01 September 2001 Publication History

Abstract

In this paper, we propose two generic text summarization methods that create text summaries by ranking and extracting sentences from the original documents. The first method uses standard IR methods to rank sentence relevances, while the second method uses the latent semantic analysis technique to identify semantically important sentences, for summary creations. Both methods strive to select sentences that are highly ranked and different from each other. This is an attempt to create a summary with a wider coverage of the document's main content and less redundancy. Performance evaluations on the two summarization methods are conducted by comparing their summarization outputs with the manual summaries generated by three independent human evaluators. The evaluations also study the influence of different VSM weighting schemes on the text summarization performances. Finally, the causes of the large disparities in the evaluators' manual summarization results are investigated, and discussions on human text summarization patterns are presented.

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Cited By

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  • (2024)Charting the Growth of Text Summarisation: A Data-Driven Exploration of Research Trends and Technological AdvancementsApplied Sciences10.3390/app14231146214:23(11462)Online publication date: 9-Dec-2024
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  • (2024)Comparison of Extractive and Abstractive Approaches in Automatic Text Summarization: An Evaluation on BBC-News and PubMed Datasets2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)10.1109/IDAP64064.2024.10710907(1-7)Online publication date: 21-Sep-2024
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Published In

cover image ACM Conferences
SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
September 2001
454 pages
ISBN:1581133316
DOI:10.1145/383952
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 September 2001

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Author Tags

  1. generic text summarization
  2. relevance measure
  3. semantic analysis

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SIGIR '01 Paper Acceptance Rate 47 of 201 submissions, 23%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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  • (2024)Charting the Growth of Text Summarisation: A Data-Driven Exploration of Research Trends and Technological AdvancementsApplied Sciences10.3390/app14231146214:23(11462)Online publication date: 9-Dec-2024
  • (2024)Online Summarization of Microblog Data: An Aid in Handling Disaster SituationsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.334752011:3(4029-4039)Online publication date: Jun-2024
  • (2024)Comparison of Extractive and Abstractive Approaches in Automatic Text Summarization: An Evaluation on BBC-News and PubMed Datasets2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)10.1109/IDAP64064.2024.10710907(1-7)Online publication date: 21-Sep-2024
  • (2024)A Hybrid Strategy for Chat Transcript SummarizationIEEE Access10.1109/ACCESS.2024.347396812(146620-146634)Online publication date: 2024
  • (2024)Abstractive Summarization Model for Summarizing Scientific ArticleIEEE Access10.1109/ACCESS.2024.342016312(91252-91262)Online publication date: 2024
  • (2024)Feature-Based Text Search Engine Mitigating Data Diversity Problem Using Pre-Trained Large Language Model for Fast Deployment ServicesIEEE Access10.1109/ACCESS.2024.337347012(48145-48157)Online publication date: 2024
  • (2024)Abstractive text summarization: State of the art, challenges, and improvementsNeurocomputing10.1016/j.neucom.2024.128255603(128255)Online publication date: Oct-2024
  • (2024)Supervised weight learning-based PSO framework for single document extractive summarizationApplied Soft Computing10.1016/j.asoc.2024.111678161(111678)Online publication date: Aug-2024
  • (2024)ADSumm: annotated ground-truth summary datasets for disaster tweet summarizationSocial Network Analysis and Mining10.1007/s13278-024-01323-914:1Online publication date: 5-Aug-2024
  • (2024)Multilingual Summarization for German TextsProceedings of International Conference on Computational Intelligence10.1007/978-981-97-3526-6_46(599-616)Online publication date: 18-Jul-2024
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