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

Yuliska; Tetsuya Sakai

doi:10.1109/INFOCT.2019.8710851

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

Yuliska, Tetsuya Sakai

School of Fundamental Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	153-157
Number of pages	5
ISBN (Electronic)	9781728133232
DOIs	https://doi.org/10.1109/INFOCT.2019.8710851
Publication status	Published - 2019 May 9
Event	2nd IEEE International Conference on Information and Computer Technologies, ICICT 2019 - Kahului, United States Duration: 2019 Mar 14 → 2019 Mar 17

Publication series

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

Conference

Conference	2nd IEEE International Conference on Information and Computer Technologies, ICICT 2019
Country	United States
City	Kahului
Period	19/3/14 → 19/3/17

Fingerprint

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 proceeding › Conference 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).

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Access to Document

10.1109/INFOCT.2019.8710851