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Extractive Incremental Multi-Document Summarization by Ranking Sentences Relevant to Key Phrase

J.Tamilselvan 1 , A.Senthilrajan 2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-12 , Page no. 250-253, Dec-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i12.250253

Online published on Dec 31, 2018

Copyright © J.Tamilselvan, A.Senthilrajan . 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: J.Tamilselvan, A.Senthilrajan, “Extractive Incremental Multi-Document Summarization by Ranking Sentences Relevant to Key Phrase,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.250-253, 2018.

MLA Style Citation: J.Tamilselvan, A.Senthilrajan "Extractive Incremental Multi-Document Summarization by Ranking Sentences Relevant to Key Phrase." International Journal of Computer Sciences and Engineering 6.12 (2018): 250-253.

APA Style Citation: J.Tamilselvan, A.Senthilrajan, (2018). Extractive Incremental Multi-Document Summarization by Ranking Sentences Relevant to Key Phrase. International Journal of Computer Sciences and Engineering, 6(12), 250-253.

BibTex Style Citation:
@article{_2018,
author = {J.Tamilselvan, A.Senthilrajan},
title = {Extractive Incremental Multi-Document Summarization by Ranking Sentences Relevant to Key Phrase},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2018},
volume = {6},
Issue = {12},
month = {12},
year = {2018},
issn = {2347-2693},
pages = {250-253},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3325},
doi = {https://doi.org/10.26438/ijcse/v6i12.250253}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i12.250253}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3325
TI - Extractive Incremental Multi-Document Summarization by Ranking Sentences Relevant to Key Phrase
T2 - International Journal of Computer Sciences and Engineering
AU - J.Tamilselvan, A.Senthilrajan
PY - 2018
DA - 2018/12/31
PB - IJCSE, Indore, INDIA
SP - 250-253
IS - 12
VL - 6
SN - 2347-2693
ER -

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Abstract

The summarization deal’s with giving the concepts precisely. The multi-document summarization gives the extract of the multiple documents into summarized single document. Here we summarize the document individually by extracting the key phrase using the RAKE algorithm, which perform well on the single document and does not depend on the corpus. This enables the reader to find out the documents, which are highly related to the document by using the TextRank algorithm that ranks the sentence based on the key phrase selected from the single document and they can read the entire document without going through all. The work finds the summary from the given documents and those are ranked and the high ranked documents selected are then used as input to the documents at the next level. The information gained from the previous level (i.e. Summary from documents) are used as the input for the next phase, which will give more information.

Key-Words / Index Term

Multi Document Summarization, Extraction, Sentence Ranking

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