A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization

Yuliska, Tetsuya Sakai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Query-focused multi-document summarization aims to produce a single, short document that summarizes a set of documents that are relevant to a given query. During the past few years, deep learning approaches have been utilized to generate summaries in an abstractive or extractive manner. In this study, we employ six deep neural network approaches to solving a query-focused extractive multi-document summarization task and compare their performances. To the best of our knowledge, our study is the first to compare deep learning techniques on extractive query-focused multi-document summarization. Our experiments with DUC 2005-2007 benchmark datasets show that Bi-LSTM with Max-pooling achieves the highest performance among the methods compared.

Original languageEnglish
Title of host publication2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages153-157
Number of pages5
ISBN (Electronic)9781728133232
DOIs
Publication statusPublished - 2019 May 9
Event2nd IEEE International Conference on Information and Computer Technologies, ICICT 2019 - Kahului, United States
Duration: 2019 Mar 142019 Mar 17

Publication series

Name2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019

Conference

Conference2nd IEEE International Conference on Information and Computer Technologies, ICICT 2019
CountryUnited States
CityKahului
Period19/3/1419/3/17

Fingerprint

learning
neural network
Experiments
performance
Summarization
Comparative study
Deep learning
Query
experiment
Deep neural networks
Neural networks
Benchmark
Pooling
Experiment
High performance

Keywords

  • comparative study
  • deep neural network
  • extractive summarization
  • query-focused summarization
  • text summarization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Social Sciences (miscellaneous)
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

Cite this

Yuliska, & Sakai, T. (2019). A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization. In 2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019 (pp. 153-157). [8710851] (2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFOCT.2019.8710851

A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization. / Yuliska, ; Sakai, Tetsuya.

2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 153-157 8710851 (2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yuliska, & Sakai, T 2019, A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization. in 2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019., 8710851, 2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019, Institute of Electrical and Electronics Engineers Inc., pp. 153-157, 2nd IEEE International Conference on Information and Computer Technologies, ICICT 2019, Kahului, United States, 19/3/14. https://doi.org/10.1109/INFOCT.2019.8710851
Yuliska , Sakai T. A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization. In 2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 153-157. 8710851. (2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019). https://doi.org/10.1109/INFOCT.2019.8710851
Yuliska, ; Sakai, Tetsuya. / A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization. 2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 153-157 (2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019).
@inproceedings{54121f3464604894a5356c79460f415d,
title = "A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization",
abstract = "Query-focused multi-document summarization aims to produce a single, short document that summarizes a set of documents that are relevant to a given query. During the past few years, deep learning approaches have been utilized to generate summaries in an abstractive or extractive manner. In this study, we employ six deep neural network approaches to solving a query-focused extractive multi-document summarization task and compare their performances. To the best of our knowledge, our study is the first to compare deep learning techniques on extractive query-focused multi-document summarization. Our experiments with DUC 2005-2007 benchmark datasets show that Bi-LSTM with Max-pooling achieves the highest performance among the methods compared.",
keywords = "comparative study, deep neural network, extractive summarization, query-focused summarization, text summarization",
author = "Yuliska and Tetsuya Sakai",
year = "2019",
month = "5",
day = "9",
doi = "10.1109/INFOCT.2019.8710851",
language = "English",
series = "2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "153--157",
booktitle = "2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019",

}

TY - GEN

T1 - A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization

AU - Yuliska,

AU - Sakai, Tetsuya

PY - 2019/5/9

Y1 - 2019/5/9

N2 - Query-focused multi-document summarization aims to produce a single, short document that summarizes a set of documents that are relevant to a given query. During the past few years, deep learning approaches have been utilized to generate summaries in an abstractive or extractive manner. In this study, we employ six deep neural network approaches to solving a query-focused extractive multi-document summarization task and compare their performances. To the best of our knowledge, our study is the first to compare deep learning techniques on extractive query-focused multi-document summarization. Our experiments with DUC 2005-2007 benchmark datasets show that Bi-LSTM with Max-pooling achieves the highest performance among the methods compared.

AB - Query-focused multi-document summarization aims to produce a single, short document that summarizes a set of documents that are relevant to a given query. During the past few years, deep learning approaches have been utilized to generate summaries in an abstractive or extractive manner. In this study, we employ six deep neural network approaches to solving a query-focused extractive multi-document summarization task and compare their performances. To the best of our knowledge, our study is the first to compare deep learning techniques on extractive query-focused multi-document summarization. Our experiments with DUC 2005-2007 benchmark datasets show that Bi-LSTM with Max-pooling achieves the highest performance among the methods compared.

KW - comparative study

KW - deep neural network

KW - extractive summarization

KW - query-focused summarization

KW - text summarization

UR - http://www.scopus.com/inward/record.url?scp=85066608855&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066608855&partnerID=8YFLogxK

U2 - 10.1109/INFOCT.2019.8710851

DO - 10.1109/INFOCT.2019.8710851

M3 - Conference contribution

T3 - 2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019

SP - 153

EP - 157

BT - 2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019

PB - Institute of Electrical and Electronics Engineers Inc.

ER -