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Deep Learning Architecture for Multi-Document Summarization as a cascade of Abstractive and Extractive Summarization approaches

Anita Kumari Singh1 , M Shashi2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-3 , Page no. 950-954, Mar-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i3.950954

Online published on Mar 31, 2019

Copyright © Anita Kumari Singh, M Shashi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Anita Kumari Singh, M Shashi, “Deep Learning Architecture for Multi-Document Summarization as a cascade of Abstractive and Extractive Summarization approaches,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.950-954, 2019.

MLA Style Citation: Anita Kumari Singh, M Shashi "Deep Learning Architecture for Multi-Document Summarization as a cascade of Abstractive and Extractive Summarization approaches." International Journal of Computer Sciences and Engineering 7.3 (2019): 950-954.

APA Style Citation: Anita Kumari Singh, M Shashi, (2019). Deep Learning Architecture for Multi-Document Summarization as a cascade of Abstractive and Extractive Summarization approaches. International Journal of Computer Sciences and Engineering, 7(3), 950-954.

BibTex Style Citation:
@article{Singh_2019,
author = {Anita Kumari Singh, M Shashi},
title = {Deep Learning Architecture for Multi-Document Summarization as a cascade of Abstractive and Extractive Summarization approaches},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2019},
volume = {7},
Issue = {3},
month = {3},
year = {2019},
issn = {2347-2693},
pages = {950-954},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3945},
doi = {https://doi.org/10.26438/ijcse/v7i3.950954}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i3.950954}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3945
TI - Deep Learning Architecture for Multi-Document Summarization as a cascade of Abstractive and Extractive Summarization approaches
T2 - International Journal of Computer Sciences and Engineering
AU - Anita Kumari Singh, M Shashi
PY - 2019
DA - 2019/03/31
PB - IJCSE, Indore, INDIA
SP - 950-954
IS - 3
VL - 7
SN - 2347-2693
ER -

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Abstract

Document summarizers create a shorter compressed version of a text document automatically. Summaries are created to give the gist of the entire document covering the key points of the document with improved readability while avoiding redundancy. Abstractive summarization synthesizes summary statements of a given document and is presently limited to single document summarization. The proposed model extends the applicability of abstractive summarization for multi-document texts by proposing a new Deep Learning architecture as a cascade of Abstractive and Extractive summarization. The proposed hybrid architecture is used to generate compact and comprehensive summaries from multiple news articles published on specific topics. The architecture was evaluated using DUC 2004 data and its performance is found to be better compared to traditional Multi Document Extractive Summarization methods in terms of ROUGE scores.

Key-Words / Index Term

Document Summarization, Abstractive, Extractive, ROUGE

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