Summary Generation Using Natural Language Processing Techniques and Cosine Similarity

Sayantan Pal; Maiga Chang; Maria Fernandez Iriarte

doi:10.1007/978-3-030-96308-8_47

Summary Generation Using Natural Language Processing Techniques and Cosine Similarity

Sayantan Pal
, Maiga Chang
, Maria Fernandez Iriarte

School of Computing and Information systems

Maulana Abul Kalam Azad University of Technology
Athabasca University

Research output: Chapter in Book/Report/Conference proceeding › Published Conference contribution › peer-review

10 Citations (Scopus)

Abstract

The COVID-19 pandemic has led to an unprecedented challenge to public health. It resulted in global efforts to understand, record, and alleviate the disease. This research serves the purpose of generating a relevant summary related to Coronavirus. The research uses the COVID-19 Open Research Dataset (CORD-19) provided by Allen Institute for AI. The dataset contains 236,336 academic full-text articles as of July 19, 2021. This paper introduces a web-based system to handle user questions over the Coronavirus full-text scholarly articles. The system periodically runs backend services to process such large amount article with basic Natural Language Processing (NLP) techniques that include tokenization, N-Grams extraction, and part-of-speech (PoS) tagging. It automatically identifies the keywords from the question and uses cosine similarity to summarize the associated content and present to the user. This research will possibly benefit researchers, health workers as well as other individuals. Moreover, the same service can be used to train with the datasets of different domains (e.g., education) to generate a relevant summary for other user groups (e.g., students).

Original language	English
Title of host publication	Intelligent Systems Design and Applications - 21st International Conference on Intelligent Systems Design and Applications, ISDA 2021
Editors	Ajith Abraham, Niketa Gandhi, Thomas Hanne, Tzung-Pei Hong, Tatiane Nogueira Rios, Weiping Ding
Pages	508-517
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-030-96308-8_47
Publication status	Published - 2022
Event	21st International Conference on Intelligent Systems Design and Applications, ISDA 2021 - Virtual, Online Duration: 13 Dec. 2021 → 15 Dec. 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	418 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	21st International Conference on Intelligent Systems Design and Applications, ISDA 2021
City	Virtual, Online
Period	13/12/21 → 15/12/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Coronavirus
Information extraction
N-grams
Parts of speech
Question and answering

Access to Document

10.1007/978-3-030-96308-8_47

Cite this

Pal, S., Chang, M., & Iriarte, M. F. (2022). Summary Generation Using Natural Language Processing Techniques and Cosine Similarity. In A. Abraham, N. Gandhi, T. Hanne, T.-P. Hong, T. Nogueira Rios, & W. Ding (Eds.), Intelligent Systems Design and Applications - 21st International Conference on Intelligent Systems Design and Applications, ISDA 2021 (pp. 508-517). (Lecture Notes in Networks and Systems; Vol. 418 LNNS). https://doi.org/10.1007/978-3-030-96308-8_47

Pal, Sayantan ; Chang, Maiga ; Iriarte, Maria Fernandez. / Summary Generation Using Natural Language Processing Techniques and Cosine Similarity. Intelligent Systems Design and Applications - 21st International Conference on Intelligent Systems Design and Applications, ISDA 2021. editor / Ajith Abraham ; Niketa Gandhi ; Thomas Hanne ; Tzung-Pei Hong ; Tatiane Nogueira Rios ; Weiping Ding. 2022. pp. 508-517 (Lecture Notes in Networks and Systems).

@inproceedings{627340a5ee6845e8b7147f285445f1a2,

title = "Summary Generation Using Natural Language Processing Techniques and Cosine Similarity",

abstract = "The COVID-19 pandemic has led to an unprecedented challenge to public health. It resulted in global efforts to understand, record, and alleviate the disease. This research serves the purpose of generating a relevant summary related to Coronavirus. The research uses the COVID-19 Open Research Dataset (CORD-19) provided by Allen Institute for AI. The dataset contains 236,336 academic full-text articles as of July 19, 2021. This paper introduces a web-based system to handle user questions over the Coronavirus full-text scholarly articles. The system periodically runs backend services to process such large amount article with basic Natural Language Processing (NLP) techniques that include tokenization, N-Grams extraction, and part-of-speech (PoS) tagging. It automatically identifies the keywords from the question and uses cosine similarity to summarize the associated content and present to the user. This research will possibly benefit researchers, health workers as well as other individuals. Moreover, the same service can be used to train with the datasets of different domains (e.g., education) to generate a relevant summary for other user groups (e.g., students).",

keywords = "Coronavirus, Information extraction, N-grams, Parts of speech, Question and answering",

author = "Sayantan Pal and Maiga Chang and Iriarte, \{Maria Fernandez\}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 21st International Conference on Intelligent Systems Design and Applications, ISDA 2021 ; Conference date: 13-12-2021 Through 15-12-2021",

year = "2022",

doi = "10.1007/978-3-030-96308-8\_47",

language = "English",

isbn = "9783030963071",

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pages = "508--517",

editor = "Ajith Abraham and Niketa Gandhi and Thomas Hanne and Tzung-Pei Hong and \{Nogueira Rios\}, Tatiane and Weiping Ding",

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}

Pal, S, Chang, M & Iriarte, MF 2022, Summary Generation Using Natural Language Processing Techniques and Cosine Similarity. in A Abraham, N Gandhi, T Hanne, T-P Hong, T Nogueira Rios & W Ding (eds), Intelligent Systems Design and Applications - 21st International Conference on Intelligent Systems Design and Applications, ISDA 2021. Lecture Notes in Networks and Systems, vol. 418 LNNS, pp. 508-517, 21st International Conference on Intelligent Systems Design and Applications, ISDA 2021, Virtual, Online, 13/12/21. https://doi.org/10.1007/978-3-030-96308-8_47

Summary Generation Using Natural Language Processing Techniques and Cosine Similarity. / Pal, Sayantan; Chang, Maiga; Iriarte, Maria Fernandez.
Intelligent Systems Design and Applications - 21st International Conference on Intelligent Systems Design and Applications, ISDA 2021. ed. / Ajith Abraham; Niketa Gandhi; Thomas Hanne; Tzung-Pei Hong; Tatiane Nogueira Rios; Weiping Ding. 2022. p. 508-517 (Lecture Notes in Networks and Systems; Vol. 418 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Published Conference contribution › peer-review

TY - GEN

T1 - Summary Generation Using Natural Language Processing Techniques and Cosine Similarity

AU - Pal, Sayantan

AU - Chang, Maiga

AU - Iriarte, Maria Fernandez

PY - 2022

Y1 - 2022

N2 - The COVID-19 pandemic has led to an unprecedented challenge to public health. It resulted in global efforts to understand, record, and alleviate the disease. This research serves the purpose of generating a relevant summary related to Coronavirus. The research uses the COVID-19 Open Research Dataset (CORD-19) provided by Allen Institute for AI. The dataset contains 236,336 academic full-text articles as of July 19, 2021. This paper introduces a web-based system to handle user questions over the Coronavirus full-text scholarly articles. The system periodically runs backend services to process such large amount article with basic Natural Language Processing (NLP) techniques that include tokenization, N-Grams extraction, and part-of-speech (PoS) tagging. It automatically identifies the keywords from the question and uses cosine similarity to summarize the associated content and present to the user. This research will possibly benefit researchers, health workers as well as other individuals. Moreover, the same service can be used to train with the datasets of different domains (e.g., education) to generate a relevant summary for other user groups (e.g., students).

AB - The COVID-19 pandemic has led to an unprecedented challenge to public health. It resulted in global efforts to understand, record, and alleviate the disease. This research serves the purpose of generating a relevant summary related to Coronavirus. The research uses the COVID-19 Open Research Dataset (CORD-19) provided by Allen Institute for AI. The dataset contains 236,336 academic full-text articles as of July 19, 2021. This paper introduces a web-based system to handle user questions over the Coronavirus full-text scholarly articles. The system periodically runs backend services to process such large amount article with basic Natural Language Processing (NLP) techniques that include tokenization, N-Grams extraction, and part-of-speech (PoS) tagging. It automatically identifies the keywords from the question and uses cosine similarity to summarize the associated content and present to the user. This research will possibly benefit researchers, health workers as well as other individuals. Moreover, the same service can be used to train with the datasets of different domains (e.g., education) to generate a relevant summary for other user groups (e.g., students).

KW - Coronavirus

KW - Information extraction

KW - N-grams

KW - Parts of speech

KW - Question and answering

UR - https://www.scopus.com/pages/publications/85127721344

U2 - 10.1007/978-3-030-96308-8_47

DO - 10.1007/978-3-030-96308-8_47

M3 - Published Conference contribution

AN - SCOPUS:85127721344

SN - 9783030963071

T3 - Lecture Notes in Networks and Systems

SP - 508

EP - 517

BT - Intelligent Systems Design and Applications - 21st International Conference on Intelligent Systems Design and Applications, ISDA 2021

A2 - Abraham, Ajith

A2 - Gandhi, Niketa

A2 - Hanne, Thomas

A2 - Hong, Tzung-Pei

A2 - Nogueira Rios, Tatiane

A2 - Ding, Weiping

T2 - 21st International Conference on Intelligent Systems Design and Applications, ISDA 2021

Y2 - 13 December 2021 through 15 December 2021

ER -

Pal S, Chang M, Iriarte MF. Summary Generation Using Natural Language Processing Techniques and Cosine Similarity. In Abraham A, Gandhi N, Hanne T, Hong TP, Nogueira Rios T, Ding W, editors, Intelligent Systems Design and Applications - 21st International Conference on Intelligent Systems Design and Applications, ISDA 2021. 2022. p. 508-517. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-96308-8_47

Summary Generation Using Natural Language Processing Techniques and Cosine Similarity

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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