Trends and Applications of Text Summarization Techniques

357 Pages·2019·10.035 MB·English

by  Alessandro Fiori (editor)

Trends and Applications
of Text Summarization
Techniques
Alessandro Fiori
Candiolo Cancer Institute – FPO, IRCCS, Italy
A volume in the Advances in Data
Mining and Database Management
(ADMDM) Book Series
Published in the United States of America by
IGI Global
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Library of Congress Cataloging-in-Publication Data
Names: Fiori, Alessandro, 1982- editor.
Title: Trends and applications of text summarization techniques / Alessandro
Fiori, editor.
Description: Hershey, PA : Engineering Science Reference, [2020] | Includes
bibliographical references.
Identifiers: LCCN 2019001873| ISBN 9781522593737 (hardcover) | ISBN
9781522593744 (softcover) | ISBN 9781522593751 (ebook)
Subjects: LCSH: Automatic abstracting. | Electronic information
resources--Abstracting and indexing. | Text processing (Computer science)
Classification: LCC Z695.92 .T74 2020 | DDC 025.4/10285635--dc23 LC record available at
https://lccn.loc.gov/2019001873

This book is published in the IGI Global book series Advances in Data Mining and Database
Management (ADMDM) (ISSN: 2327-1981; eISSN: 2327-199X)
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Advances in Data Mining
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ISSN:2327-1981
EISSN:2327-199X
Editor-in-Chief: David Taniar, Monash University, Australia
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Editorial Advisory Board
Silvia Chiusano, Politecnico di Torino, Italy
Paolo Garza, Politecnico di Torino, Italy
George Giannakopoulos, NCSR Demokritos, Greece
Saima Jabeen, University of Wah, Pakistan
Jochen L. Leidner, Refinitiv Labs, UK & University of Sheffield, UK
Andrea Mignone, Candiolo Cancer Institute (FPO IRCCS), Italy
Josef Steinberger, University of West Bohemia, Czech Republic
Table of Contents
Foreword.............................................................................................................xiv
Preface..................................................................................................................xv
Acknowledgment.................................................................................................xx
Section 1
Concepts and Methods
Chapter 1
Combining.Machine.Learning.and.Natural.Language.Processing.for.
Language-Specific,.Multi-Lingual,.and.Cross-Lingual.Text.Summarization:.
A.Wide-Ranging.Overview....................................................................................1
Luca Cagliero, Politecnico di Torino, Italy
Paolo Garza, Politecnico di Torino, Italy
Moreno La Quatra, Politecnico di Torino, Italy
Chapter 2
The.Development.of.Single-Document.Abstractive.Text.Summarizer.During.
the.Last.Decade.....................................................................................................32
Amal M. Al-Numai, King Saud University, Saudi Arabia
Aqil M. Azmi, King Saud University, Saudi Arabia
Chapter 3
Mining.Scientific.and.Technical.Literature:.From.Knowledge.Extraction.to.
Summarization......................................................................................................61
Junsheng Zhang, Institute of Scientific and Technical Information of
China, China
Wen Zeng, Institute of Scientific and Technical Information of China,
China

Chapter 4
Data.Text.Mining.Based.on.Swarm.Intelligence.Techniques:.Review.of.Text.
Summarization.Systems........................................................................................88
Mohamed Atef Mosa, Institute of Public Administration, Department of
Information Technology, Riyadh, Saudi Arabia
Chapter 5
Named.Entity.Recognition.in.Document.Summarization..................................125
Sandhya P., Vellore Institute of Technology, Chennai Campus, Tamil
Nadu, India
Mahek Laxmikant Kantesaria, Vellore Institute of Technology, Chennai
Campus, Tamil Nadu, India
Section 2
Domain Applications
Chapter 6
Text.Classification.and.Topic.Modeling.for.Online.Discussion.Forums:.An.
Empirical.Study.From.the.Systems.Modeling.Community................................151
Xin Zhao, University of Alabama, USA
Zhe Jiang, University of Alabama, USA
Jeff Gray, University of Alabama, USA
Chapter 7
Summarization.in.the.Financial.and.Regulatory.Domain...................................187
Jochen L. Leidner, Refinitiv Labs, UK & University of Sheffield, UK
Chapter 8
Opinion.Mining.and.Product.Review.Summarization.in.E-Commerce.............216
Enakshi Jana, Pondicherry University, India
V. Uma, Pondicherry University, India
Chapter 9
Scaling.and.Semantically-Enriching.Language-Agnostic.Summarization.........244
George Giannakopoulos, NCSR Demokritos, Greece & SciFY PNPC,
Greece
George Kiomourtzis, SciFY PNPC, Greece & NCSR Demokritos,
Greece
Nikiforos Pittaras, NCSR Demokritos, Greece & National and
Kapodistrian University of Athens, Greece
Vangelis Karkaletsis, NCSR Demokritos, Greece

Compilation of References...............................................................................293
About the Contributors....................................................................................329
Index...................................................................................................................334
Detailed Table of Contents
Foreword.............................................................................................................xiv
Preface..................................................................................................................xv
Acknowledgment.................................................................................................xx
Section 1
Concepts and Methods
Chapter 1
Combining.Machine.Learning.and.Natural.Language.Processing.for.
Language-Specific,.Multi-Lingual,.and.Cross-Lingual.Text.Summarization:.
A.Wide-Ranging.Overview....................................................................................1
Luca Cagliero, Politecnico di Torino, Italy
Paolo Garza, Politecnico di Torino, Italy
Moreno La Quatra, Politecnico di Torino, Italy
The.recent.advances.in.multimedia.and.web-based.applications.have.eased.the.
accessibility.to.large.collections.of.textual.documents..To.automate.the.process.of.
document.analysis,.the.research.community.has.put.relevant.efforts.into.extracting.
short.summaries.of.the.document.content..However,.most.of.the.early.proposed.
summarization.methods.were.tailored.to.English-written.textual.corpora.or.to.
collections.of.documents.all.written.in.the.same.language..More.recently,.the.joint.
efforts.of.the.machine.learning.and.the.natural.language.processing.communities.have.
produced.more.portable.and.flexible.solutions,.which.can.be.applied.to.documents.
written.in.different.languages..This.chapter.first.overviews.the.most.relevant.language-
specific.summarization.algorithms..Then,.it.presents.the.most.recent.advances.in.
multi-.and.cross-lingual.text.summarization..The.chapter.classifies.the.presented.
methodology,.highlights.the.main.pros.and.cons,.and.discusses.the.perspectives.of.
the.extension.of.the.current.research.towards.cross-lingual.summarization.systems.