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IoT 24(1):

Research Article

Sentence Fusion using Deep Learning

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  • @ARTICLE{10.4108/eetiot.4605,
        author={Sohini Roy Chowdhury and Kamal Sarkar},
        title={Sentence Fusion using Deep Learning},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={IOT},
        year={2023},
        month={12},
        keywords={Abstractive Summarization, Deep Learning, Sentence Fusion},
        doi={10.4108/eetiot.4605}
    }
    
  • Sohini Roy Chowdhury
    Kamal Sarkar
    Year: 2023
    Sentence Fusion using Deep Learning
    IOT
    EAI
    DOI: 10.4108/eetiot.4605
Sohini Roy Chowdhury1, Kamal Sarkar1,*
  • 1: Jadavpur University
*Contact email: jukamal2001@yahoo.com

Abstract

The human process of document summarization involves summarizing a document by sentence fusion. Sentence fusion combines two or more sentences to create an abstract sentence. Sentence fusion is useful to convert an extractive summary to an abstractive summary. The extractive summary contains a set of salient sentences selected from a single document or multiple related documents. Redundancy creates problems while creating an extractive summary because it contains sentences whose segments or phrases are redundant. Sentence fusion helps to remove redundancy by fusing sentences into a single abstract sentence. This moves an extractive summary to an abstractive summary. In this paper, we present an approach that uses a deep learning model for sentence fusion. which is trained over a large dataset. We have tested our approach through both manual evaluation and system evaluation. The result of our proposed approach shows that our model is good enough to fuse sentences effectively.

Keywords
Abstractive Summarization, Deep Learning, Sentence Fusion
Received
2023-10-06
Accepted
2023-12-10
Published
2023-12-14
Publisher
EAI
http://dx.doi.org/10.4108/eetiot.4605

Copyright © 2023 S. Roy Chowdhury et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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